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I. Introduction

Smart homes, the next gigantic leap in the field of home automation, have become an emerging research field in last few decades. Research on smart homes has been gradually moving towards application of ubiquitous computing, tackling issues on device heterogeneity and interoperability. A smart home adjusts its function to the inhabitant’s need according to the information it collects from inhabitants, the computation system and the context [1].

By 2050, approximately 20% of the world population will be at least 60 years old [2]. This age group is more likely to suffer from long-term chronic diseases and will face difficulties in living independently. According to World Health Organization (WHO), 650 million people live with disabilities around the world [3].The most common causes of disability include chronic diseases such as diabetes, cardiovascular disease and cancer; injuries due to road traffic crashes, conflicts, falls, landmines, mental impairments, birth defects, malnutrition, HIV/AIDS and other communicable diseases. It is not possible and logical to support all these patients in the medical center or nursing homes for an uncertain period of time. The solution is to accommodate health care services and assistive technologies in their home environment which is the main objective of smart homes.

Sensors, multimedia devices and physiological equipments are core components to perceive information from home environment Infrared (IR) sensors, pressure sensors, magnetic contacts, passive and active Radio Frequency Identification (RFID) tags are used to track inhabitant location detection. Electrocardiogram (ECG), photoplethysmograph(PPG), ,temperature, spirometry, galvanic skin response, colorimetry and pulse measurement equipments are used to get physiological information from the patient. Camera and microphones provide audiovisual response from home user. Inhabitant can access the system through display panel.

Power line communication protocols are widely used for the connectivity of home appliances. Public telecommunication network with voice and text messaging service is involved to provide telecare facility from remote location. Videoconferencing is used as an interactive communication media between caregiver and the client. TCP/IP protocols of Ethernet network provide data connectivity for local and remote sites and locations. Ethernet protocols are also used to connect health-monitoring equipments and to provide data repository service.

Algorithms from machine learning, data compression, statistics and artificial intelligent are employed to predict user behavior, detect activities of daily life (ADL) and location. C4.5 algorithm from machine learning is utilized to build spatiotemporal context of user. C4.5 algorithm is developed by Quinlan in 1993 which classify the data to construct a decision tree according to data attributes [47].Active LeZi from data compression algorithms is used to predict inhabitant’s next behavior. Active LeZi by Gopalratnam et al..in 2007 builds a decision tree utilizing similar methodology of LZ78 data compression algorithm and predict next event using Prediction by Partial Matching (PPM) algorithm[22].Statistical predictive algorithms like Bayesian filtering, dynamic Bayesian network algorithms classify the information and recognize ADL of home client[34][41][44]. Different flavors of AI algorithms extended for smart home data processing. Markov model, Hidden Markov model, Artificial Neural Network can detect the living pattern of user and can also predict the user [7][13][14][38]. Fuzzy Logic is used for home appliance control [36].

Smart home is mainly dedicated to provide health care, safety, security and monitoring service for patient and elderly. The house is equipped with sensors, cameras to track people and can trigger an alarm to a remote heath care service provider in the case of emergency. Sophisticated physiological devices monitor heart rate, blood pressure, body temperature, ECG record and the patient is being observed from a distance location. Telecommunication service is used for communicating with service provider, relatives or neighbor and as a redundant acknowledgement method from the patient. For home comfort system, lighting, heating, doors, windows and home appliances are automatically controlled by ambient intelligence of smart home. Smart home also has significant contribution towards energy conservation by integration of energy meter with smart home [4].

Home automation is the initial state of smart home where electronic technologies are used to provide an easy access to household devices. Rapid development of sensor technology accelerated the growth of smart home that involved more data processing. Improvement of information and communication technology make possible to develop easy and cost effective methods for data repository and exchange. Smart home is a growing concept, efficient and lower cost solutions for general people are the main idea to promote it.

II. Smart Home defination

Smart home is an extension of modern electronic, information and communication technologies. The main objective of smart home research is to provide smartness to a dwelling facility for comfort, healthcare, security and energy conservation. Remote monitoring system is a common component of health smart home where telecommunication and web technologies are used to provide quick and proper medication to the patient from specialized assistance centre.

The first formal definition of smart home was published by Intertek in 2003, which was involved to Department of Trade and Industry (DTI) smart-homes project in UK [5]. According to Intertek a smart home is a dwelling incorporating a communications network that connects the key electrical appliances and services, and allows them to be remotely controlled, monitored or accessed. A home needs three things to make it smart:

  1. Internal network – wire, cable, wireless
  2. Intelligent control – gateway to manage the systems
  3. Home automation – products within the homes and links to services and systems outside the home

III. Review of Smart Homes

Smart homes projects are being conducted for last several decades and they convey different ideas, functions and utilities. It is growing to different brunches of specialization focusing the interest of the researchers and user requirements and expectations. This article is a study of the evolution of smart home according to time.

Adaptive Control of Home Environment (ACHE) system is developed by Mozer in 1998 in USA. ACHE monitors user device usage pattern utilizing different types of sensors and builds an adaptive inferential engine for neural network to control temperature, heating and lighting. ACHE can control three main components of a home while trying to maximize user comfort and conserve energy [7].ACHE is one of the early smart home projects which is able to partially automate home environment via controlling lighting, temperature and heating components.

CarerNet is an architectural model of integrated and intelligent telecare system proposed by Williams et al. in 1998. Its core components are sensor set, a sensor bus, intelligent monitoring system and a control unit. ECG, photoplethysmograph, spirometry, temperature, galvanic skin response, colorimetry, and pulse measurement tools used to collect physiological data. The communication network within the client’s local environment is an integration of HomeLAN and Body Area Network (BAN) which is responsible to carry real-time data, event data, command and control data. It has a distributed intelligence system in the form of smart sensors, smart therapy units, body-hub, Local Intelligence Unit (LIU) and Client’s Healthcare Record (CHR). Home emergency alarm system, community health information and ambulatory monitoring service can be provided by the system. [8]. CarerNet is an abstract model of health smart home and interconnecting components. No prototype of the model has been developed. Only a hypothetical case study is of an individual who had undergone brain surgery after suffering from a subarachnoid is discussed.

Barnes et al. in 1998 have evaluated life style monitoring data of elderly using infrastructure of British Telecom and Anchor Trust in England. The system detects inhabitants’ movement using IR sensors and magnetic contacts on the entrance of the doors. To measure temperature it uses a temperature sensor in the main living area. An alarm activation system is developed which detects abnormal behavior and communicates to remote telecare control center, the clients and their carers[9]. The researchers presented a lower cost solution for smart telecare. The limitation of the system is it can identify only abnormal sleeping duration, unexpected inactivity, uncomfortable home temperature and fridge usage disorder. Moreover, it uses a special new telecom protocol named “No Ring Calling? which demands modifying existing telecom protocols.

TERVA is a health monitoring system developed in Finland by Korhonen et al.(1998). TERVA processes physiological information like blood pressure, heart beat rate, body temperature, body weight to draw graphical representation of wellness condition of the subject[10].Research goal of the TERVA system is to develop a real time visual monitoring system but it is unable to provide long-term trend of certain physiological information. It cannot detect physiological problems and no assistive service is deployed to provide health care.

The intelligent home (IHome) project at the University of Massachusetts at Amherst has developed an intelligent environment (Lesser et al.1999) IHome is a simulated environment designed with Multi Agent Survivability Simulator (MASS) and a Java Agent Framework (JAF) as tools to evaluate agent behavior and their coordination. The focus of the project is to model agent interactions and task interactions so that the agent can evaluate the tradeoff between robustness and efficiency [11].IHome is a simulation only solution, the project never build a practical smart home to evaluate their model.

The Aware Home Research at the Georgia Institute of Technology developed a smart home, which equipped with monitoring facilities to study human behavior (Kidd et al. 1999). To build a model of user behavior pattern, it uses smart floor to sense footsteps. Hidden Markov models, simple feature-vector averaging and neural network algorithms are applied on these data to create and evaluate behavioral model [12]. The aim of the project is to study user behavior, which is the primary stage of smart home research. The project never developed home intelligence which is a big shortfall of the research.

The EasyLiving project at Microsoft Research based on intelligent environment to track multiple residents using distributed image-processing system (Krumm et al. 2000). The system can identify residents through active badge system. Measurements are used to define geometric relationship between the people, devices, places and things [13][14].The system is workable in single room only and can track upto three peoples simultaneously.

SELF (Sensorized Environment for LiFe), is an intelligent environment, which enables a person to maintain his or her health through “self-communication? (Nishida et al. 2000). SELF observes the person’s behavior with distributed sensors invisibly embedded in the daily environment, extracts physiological parameters from it, analyzes the parameters, and accumulates the results. The accumulated results are used for reporting useful information to maintain the person’s health. The researchers constructed a model room for SELF consisted of a bed with pressure sensor array, a ceiling lighting dome with a microphone and a washstand with display[15].SELF describes a self-assessment system of human health but measuring only respiratory system and sleeping disorder, which is not sufficient to monitor health condition.

The ENABLE project was set up in 2001 to measure the impact of assistive technology on the patient suffering from mild or moderate dementia (Adlam et al. 2004). The researcher installed two devices (cooker and night light) in the apartment of several patients in different locations to evaluate the efficiency of the system [16].The research scope is limited to only two household devices but to assist this type of patient the whole house must possess some kind of intelligence.

Health Integrated Smart Home Information System (HIS) is an experimental platform for home based monitoring (Virone.et al. 2002). IR sensors are used to track inhabitant activities and the information is transmitted via Controller Area Network (CAN) to a local computer. The system generates alerts according to some predefine zones [17]. The research is only limited to single inhabitant monitoring.

In 2002, Guillén et al. developed a system composed of two parts: home station (HS) and caregiver medical center (CMC) connected via integrated service digital network (ISDN) backbone. The home station is equipped with vital signs recording module to monitor physiological data like blood pressure, temperature, ECG, pulse oximetry. Caregiver medical center is like a call center designed specially with patient monitoring software. An interactive communication system between home and caregiver center is developed using videoconferencing technology [18]. Figure 1 shows functional modules of multimedia smart home. The system requires high Internet bandwidth for videoconferencing, which needs expensive equipments and high maintenance cost.

Functional module of multimedia platform [18]

At University of Tokyo, Noguchi et al (2002) designed an intelligent room to support daily life of the inhabitant. The system has three main components: data collection, data processing and integration of processed data. The system learns current state of environment from sensors attached to bed, floor, table and switches. A summarization algorithm is used to track any changes in the system. The algorithm segments the collected sensory data at the points where sensor outputs changes drastically (i.e. pressure data appears suddenly or switch sensors are changed). It labels the segment with the “room state?. It joins a state of each segment to quantize the accumulated data and ties up the changed situation. The algorithm also tries to eliminate and reduces situations that changes slightly [19].The proposed summarization algorithm can detect user activities which is tested for single room only. No home automation method discussed utilizing the algorithm.

MavHome (Managing an Adaptive Versatile Home) first introduced by Das et al. in 2002 at the University of Texas, Arlington [20]. Figure 2 describes MavHome architecture in brief. MavHome use multi disciplinary technologies: artificial intelligent, multimedia technology, mobile computing and robotics. It is divided into four abstract layers: physical, communication, information and decision. X10 protocol is used to control and monitor more than sixty X10 devices plugged into the home electric wiring system [21]. Active LeZi algorithm is developed that makes a decision tree based on kth order Markov model and predict next action calculating probability of all actions applying prediction by partial matching method [22]. Although MovHome utilize algorithms to make accurate prediction and decision, it only predicts the behavior of single inhabitant [23].

concrete architecture of MavHome[21]

The Rehabilitation Engineering Research Center on Technology for Successful aging (RERC-Tech-Aging) at the University of Florida introduced “House of Matilda? (Helel et al. 2003, 2005)[24].The home is inhabited by a dummy called Mutilda. The main aim of this research is to perceive user location using ultrasound technology. After two years, in 2005 they designed the second generation of this home named “GatorTech’[25]. GatorTech is actually integration of smart device with sensors and actuators to optimize the comfort and safety of older peoples. The system is not user friendly because it requires wearable device for user tracking.

In 2004, Mihailidis et al. developed a computer vision system in pervasive healthcare systems. The vision system consists of three agents: sensing, planning and prompting. Statistics and physics based methods of segmenting skin color in digital images are used for face and hand tracing [26]. Only hand and face tracing is not sufficient to make an efficient smart home system, the system should include body tracking and hand gesture reorganization.

Multimedia Laboratories, NTT DoCoMo Inc. in Japan, has developed a system for modeling and recognizing personal behavior utilizing sensors and Radio Frequency Identification (RFID) tag (Isoda et al. 2004)[27]. C4.5 algorithm is used to construct decision tree from the data obtained from the sensors and RFID tags. The user’s behavioral context at any given moment is obtained by matching the most recently detected states with previously defined task models. The system is an effective way for acquiring user’s spatiotemporal context but no intelligent system is developed for home appliances control.

Andoh et al. in 2004 developed a networked non-invasive health monitoring system analyzing breath rate, heart rate, snoring and body movement. Researchers adopted Ethernet network for breath monitoring system implementation. The system can estimate sleep stages analyzing data using the algorithm developed for the purpose [28]. The system cannot summarize long term observation of patient’s sleeping disorder.

In 2005, Masuda et al. have developed a health monitoring arrangement using existing telecommunication system for home visit rehabilitation therapists. Researchers used an air filled mat to measure heartbeat and respiratory condition. When the patient lies on the air mat, his heartbeat and respiratory movement cause significant change in air pressure inside the mat, which is measured by pressure sensor and analyzed by appropriate filtering process [29]. The interesting part of the project is the usage of an air bag as monitoring equipment but its limitation is, it can only measure heart rate and respiratory condition.

In 2005, Ma et al emphasized on context awareness to provide automatic services in smart home. They used case-based reasoning (CBR) to provide more appropriate services. CBR technique relies on previous interactions and experiences to find solutions for current problems. The system can adopt any manual adjustment done by modifying case data [30].This is the initial state of the project where few scenarios like AC, TV, lamp interaction is evaluated. Their future plan is to add more contexts and enrich the features of case tables.

The House_n group at MIT designed PlaceLab a new "living laboratory" for the study of ubiquitous technologies in home environment (Intille et al. 2005). PlaceLab deployed with numerous wire, light, pressure, temperature water, gas, current sensors with video and audio devices to create vast amount of real life data from single volunteers as well as couples [31].The goal of the project is to study human behavior, influence of technology on the people and how technology can be used to simplify user interaction with home appliances. Their main contribution is an open online database of smart home sensor events and a well featured analyzing software [48].Researchers never implemented the study to build an autonomous intelligent home.

Yamazaki (2006) constructed "Ubiquitous Home," a real-life test bed, for home context-aware service. It is a housing test facility for the creation of useful new home services by linking devices, sensors, and appliances across data networks. Active and passive RFID tags located above the ceiling and at the entrance of the door are used to detect and recognize inhabitants. Pressure sensors are used to track user movement and furniture. The system is occupied with plasma panels, liquid crystal display and microphone for better interaction with the users. A network robot is employed to perform certain home services. Researchers concluded that the goal of smart home is not to design an automated home but to develop an environment using interface technologies between human and the system [32].Although, the researchers installed enough sensors and interfacing devices , the system is only sensible to few task automations like TV program selection, cooking recipe display and forgotten property service.

Ha et al. (2006) presents a sensor-based indoor location-aware system that can identify resident's location. Researchers used an array of Pyroelectric Infrared (PIR) sensor and proposed a framework of smart home location aware system. An algorithm is developed to process the information collected from PIR sensors for inhabitant location detection. Their next step is to design an algorithm to determine location and trajectory of multiple residents simultaneously [33]. The project in dedicated to user location detection system which is an essential part of smart home. No system is developed to provide intelligence to the house employing user location.

In 2007, Rahal et al. at DOMUS laboratory, Universit´e de Sherbrooke, Canada, utilized Bayesian Filtering methods to determine location of the inhabitants. Bayes filters are efficiently used to estimate a person’s location using a set of fixed sensors. In this method, the last known position and the last sensor event are both used to estimate a new location. The algorithm based on Bayesian filtering shows a mean localization accuracy of 85% [34].This project also deals with user location detection algorithm, no home automation is developed using the processed information.

De Silva et al. (2007) have implemented an audiovisual retrieval and summarization system utilizing multimedia technology for human behavior tracking. Using a large number of cameras a hierarchical clustering of audio and video handover used to create personalized video clips. An adaptive algorithm is used for complete and compact summary of the video retrieved. Basic audio analysis methods are applied for accurate audio segmentation and source localization. An interface allowed users to incorporate their knowledge into the search process and obtain more accurate results for their queries [35].The system can track people, extract key frame, localize sound source, detect lighting change but cannot distinguished different people.

At Tampere University of Technology, Vainio et al.(2008) developed a proactive fuzzy home-control system. An adaptive algorithm applied to evaluate the test on obtained results. The goal of the research is to help elderly people live independently at home. Developed system can recognize routines and also recognize deviations from routines. The system can provide information to caregivers about living rhythm, sleeping disorders, and medicine taking of inhabitant [36]. But the system works sensibly only for lighting control.

In 2008, Swaminathan et al. proposed an object reorganization system using visual image localization and registration. Appliances are first registered in the image processing system. According to the voice command of the user, appropriate object is selected using an environmental map [27].It is actually a home automaton project using speech reorganization to receive user command and commands are executed to the objects which already known to the system.

Growing Self-Organizing Maps (GSOM) used a self-adaptive neural network to detect and recognize activities of daily life addressed by Zheng et al in 2008 [38] [39]. The GSOM follows the basic principle of the Kohonen self-organizing map with a special focus on adaptive architecture. The learning process of the GSOM is started by generating an initial network composed by four neurons on a 2-dimensional grid, followed by iteratively presenting training data samples. The system is tested in single room apartment for about two weeks where it can recognized user pattern of 22 distinct activities. Like other Self Adaptive Neural Networks (SANN), the system is depends on several learning parameters to be determined in advance such as initial learning rate and the size of the initial neighborhood. Other machine learning method must be utilized in parallel to determine optimum parameter for best performance.

In 2008, Perumal et al. from Institute of Advanced Technology of University Putra Malaysia (UPM) have presented a design and implemented Simple Object Access Protocol (SOAP) based residential engagement for smart home system’s appliances control [40]. An appliance control module based on SOAP and web services developed to solve the interoperation of various home appliances in smart home systems. Fifteen feedback based control channels implemented with residential management system through Web Services. If the residential management system experiences server downtime, the home appliances can still be controlled using alternate control mechanism with GSM network via SMS Module locally and remotely. This system offers a complete, bi-directional real-time control and monitoring of smart home systems. No security mechanism is used to protect the web server from unauthorized access.

Virone et al. present a dozens of statistical behavioral patterns obtained from an activity monitoring pilot study. The pilot study examined home activity rhythms of 22 residents in an assisted living environment with four case studies. Established behavioral patterns have been captured using custom software based on a statistical predictive algorithm that models circadian activity rhythms (CARs) and their deviations (Virone et al. 2008). The system cannot differentiate multiple inhabitants [41].

Yoo et al. examined web-based implementation possibility of a central repository to integrate the biosignal data arrives from various types of devices in a remote smart home. Medical waveform description Format Encoding Rule (MFER) standard is followed for communicating and storing the biosignal data in ubiquitous home health monitoring system. The web-based technology allowed ubiquitous access to the data from remote location. The paper presents a common data format for all types of sensor (Yoo et al. 2008)[42].Figure 3 describes functional architecture of web based data retrieval system. Information security, which is a burning issue for any web based system is not considered in this research.

A web-based architecture for transferring the measured biosignal data from the u-House to the remote central repository.

A snow-flake data model is designed by Zhang et al. in 2008 to represent the activities’ data in smart homes [43]. Sensor data are stored in the homeML structure. A new algorithm is proposed on the prediction of class labels for variable person and activities of daily life (ADL) indicating who is doing what, given the observed episode and time information. Accuracy is calculated as the proportion of the number of correctly predicted class over the total number of episodes in the evaluation dataset. The learning output in the form of a joint probability distribution is also assessed by the distance to the true underlying probability distribution, using the Euclidean metric. The smaller the distance is, the closer the learned model to the true situation. The algorithm is based on probabilistic distribution and able to predict ADL of more than one inhabitant. The result given is based on simulated data and the example shows only one task identification (‘making drink’ activities).

In 2008, Park et al. proposed a method for recognizing ADL at multiple levels of details by combining multi-view computer vision and RFID based direct sensor [44]. A hierarchical recognition scheme is proposed by building a dynamic Bayesian network (DBN) that encompasses both coarse-level and fine-level ADL recognition. Their methodology combines the two tracking technology. The system requires wearable RFID tag which is not comfortable for users.

Rashidi et al. developed CASAS at Washington State University in 2008. CASAS is an adaptive smart home that utilizes machine-learning techniques to discover patterns in user behaviour and to automatically mimic these patterns. The goal is to keep the resident in control of the automation. Users can provide feedback on proposed automation activities, modify the automation policies, and introduce new requests. In addition, CASAS can discover changes in resident's behaviour patterns automatically. Frequent and Periodic Activity Miner (FPAM) algorithm mines this data to discover frequent and periodic activity patterns. These activity patterns are modelled by their Hierarchal Activity Model (HAM), which utilizes the underlying temporal and structural regularities of activities to achieve a satisfactory automation policy. User can provide feedback on proposed automation activities, modify the automation policies, and introduce new requests [45].To make a system more interactive smart home should be equipped with voice reorganization facilities which is absent in this system.

Raad et al. developed a cost-effective user-friendly telemedicine system to serve the elderly and disabled people. An architecture of telemedicine support in smart home that consists of web and telecom interface is considered in their research (Raad et al. 2008)[46]. This system also suffers from information security issues.

PRIMA (Perception, recognition and integration for interactive environments) research group of the LIG laboratory at the INRIA Grenoble research center in France has defined a model for contextual learning in smart homes (2009). The authors developed a 3D smart environment consisting cameras, a microphone array and headset microphones for situation modeling. It relies on 3D video tracking and role detection process regarding activities of the person. Roles are learned by support vector machines (SVM). It is also capable to learn speed of the inhabitant and distance to the interacting object. Proposed system can identify situations like introduction, presentation, aperitif, game and siesta. Its error rate is very high [49].

Kim et al. developed a pyroelectric infrared (PIR) sensor based indoor location aware system (PILAS) in 2009.The system uses an array of PIR sensors attached with the ceiling and detects inhabitant’s location by combining overlapped detected areas. PIR sensors construct a virtual map of resident location transition. To improved accuracy, they applied Bayesian classifier using a multivariate Gaussian probability density function to determine the location of an inhabitant. PILAS is unable to detect multiple residents [50].

Wang et al. have developed a smart home monitoring and controlling system(2009). The system can be controlled from remote locations through an embedded controller. They have developed different GUI for mobile devices and PCs. Each device has a unique address. A new command format to control the devices is introduced. It is a complex system and not compatible to previous smart homes architectures [51].

Yongping et al. have developed an embedded web server to control equipments using Zigbee protocol (2009). For this purpose they used S3C2410 microprocessor which was programmed with Linux 2.6 kernel. To provider online access a small web server (only 60 Kbytes) named Boa is installed. An interface had also been designed to communicate with Zigbee module (MC13192).The system do possess any type of intelligence [52].

Hussain el al. have developed inhabitant identification system using wireless sensor network (WSN) and RFID sensors (2009). The system can identify user location by the intensity of the Radio Signal Strength Indicator (RSSI) of WSN. A person is recognized by attached a RFID tag. The combined reading of RSSI signal and RFID receiver can successfully identify specific location of a resident in the home. The system is limited to single person tracking [53].

At Industrial Technology Research Institute (ITRI) in Taiwan, Chen et al. developed a smart home which integrates different communication protocols to build a home network. They initiated Smart Appliance Alliance Net (SAANet) standard which combined different communication protocols like Universal Plug and Play (UPnP), Digital Living Network Alliance (DLNA), Open Service Gateway Initiative (OSGi) and so on. The researchers also developed intelligent home appliances. These appliances have microcontrollers to receive commands and send status to the system. They constructed a smart energy home lab to reduce energy wastage. The researchers only discussed about smart appliances and communication protocols. No idea provided for intelligent control of the appliances [54].

Casattenta is an ambient intelligence system developing by Earella et al. in Italy (2009). The researchers used wireless sensor network (WSN) to monitor elderly in order to recognize activity disorders like fall, immobility, reaction incapacity etc. The system utilized wearable kits to gather information about the user. TinyOS based mote in TmoteSky platform is used for this purpose. It can only identify motion based disorder. Physiological data acquisition like body temperature, heartbeat rate, blood pressure are yet to be implemented [55].

Controlling System and Status Retaining System (CSnSRS) is proposed by Kumar et al. to automate home appliances. The system consists of computer as a controlling device and uses X10 protocol to control home appliances. The researchers used a device called safe mode panel to retain the status of home appliances. The panel is useful to overcome from the situation in case of power failure which they called “Safe Mode? operation. A power saving mode is also proposed by turning off the controlling device when the appliance status is not changing. The researchers did not implement CSnSRS [56]

Arcelus et al. measured sit-to-stand (SiSt) duration to estimate a person’s physical mobility (2009).To measure SiSt duration they used pressure sensors placed underneath the bed and on the floor. The start time is determined by an algorithm based on the motion of the center of pressure (COP) on the mattress towards the front edge of the bed. The end time is estimated by using a 3rd order transfer function by modeling the foot pressure on the floor. Results show that post-hip-fracture and post-stroke adults produced longer SiSt durations than those of young and old healthy adults. To determine start or end time error, they used visual verification of video recording data. The research can not determine other characteristics of SiSt transfer such as the usage of hands to assist in the transfer, a measure of the forward trunk lean, the occurrence of unsuccessful transfers and a measure of stability in the standing position [57].

At National Taiwan University Lu et al built a prototype of smart home named “CoreLab?. They developed a location aware activity recognition system for an Attentive Home. Instead of using simple sensors, CoreLab uses integrated components called ambient-intelligence compliant objects (AICO).There are different types of AICOs for different purposes such as power usage, contact, pressure, location and motion detection AICOs. An enhanced version of naïve Bayes classification method is utilized to detect location aware activities of inhabitant. The proposed system can classify ADL of the user with a high confidence level. They developed an application named “Activity Map? to represent graphical contextual information about human and environment. The approach is focused on only single inhabitant tracking [58].

IV. Discussion

Smart home aimed to different groups of user and various services involved for providing support to these groups. Widespread utilization of sensor technology, high-end data processing devices, specialized methods and algorithms are the key elements of a smart home. A block diagram of smart system is shown in figure 4 [6].

General Organization of smart system [6]

A. User

Smart homes are aimed to provide health care support to elderly, disabled people. These types of users are normally suffering from long term diseases which do not require critical medical support. It is not possible and logical to ensure these types of health support in traditional medical centre. Smart homes are transferring these types of medical facilities to citizens dwelling places.

One of the target user groups of smart homes is the elderly people. These people do not has good sense and frequently forget about everything. Even they can not make their way to hospital. This target group requires safety, security and immediate health support in case of emergency.

Smart homes can support in daily life of disabled people like patient with bone fracture, hearing problems, blindness and mental disorder. These people need continuous monitoring under an intelligent environment.

Patient suffering from other diseases can be beneficial using this technology. Bio signal monitoring equipments can be used for smart health care support. The medial centres are planning to adopt this technology.

Smart homes can be utilized to provide services to healthy people, too. Smart homes monitors wellness condition of the healthy people and warns the resident if any vital sign found. For general people smart homes offer comfort, safety, security and energy conservation.

There are some ethical issues about privacy of human life. User private life should not be exposed to public. Although telehealth care improved the heath care service but it also creates problems as the access of information becomes simple and easy. Security issues can be eliminated by providing security mechanism that currently used in computer networks.

Users are the most important elements of smart home. Future home should also concern about satisfaction of inhabitant. The monitoring devices should be installed in such a way that inhabitant can forgot about their existent. More study is necessary about user life style, their satisfaction, requirements and adaptation to the system.

B. Devices and Equipments

Smart Homes rely on data acquisition equipments and devices to assess the environment and resident. These monitoring devices can be classified into four categories: sensors, physiological equipments, multimedia devices and motes. Table 1 gives a list of home monitoring equipments and devices.

Table 1: Smart Home monitoring equipments

Author

Category

Type

Year

Mozer [7]

sensor

light ,motion, temperature sensor

1998

Williams et al.[8]

physiological equipment

ECG, photoplethysmograph, spirometry, temperature, galvanic skin response , colorimetry and pulse measurement equipments

1998

Barnes et. al. [9]

sensor

IR sensors, magnetic contacts, temperature sensor

1998

Nishida et al [15]

sensor, multimedia device

pressure sensor, display. microphone

2000

Virone.et al.[17]

sensor

IR ,blood pressure, heart rate, weight, SaO2  sensor

2002

Guillén et al.[18]

sensor

blood pressure, temperature, ECG, pulse oximetry sensor

2002

Noguchi et al.[19]

sensor

pressure and switch sensors

2002

Helel et al. [24][25]

sensor

RFID, ultrasonic, temperature, pressure

2003

Masuda et al. [29]

sensor

pressure sensor inside a air bag

2005

Intille et al. [31]

sensor, multimedia device

light, pressure, temperature, water, gas, current sensors
video and audio recording equipment

2005

Yamazaki[32]

sensor, multimedia device

active and passive RFID tag, pressure sensor, plasma panels, liquid crystal display and microphone

2006

Ha et al. [33]

sensor

PIR sensor

2006

Rahal et al[34]

sensor

IR, tactile carpets  light switches 
door contacts, pressure sensor

2007

De Silva et al.[35]

multimedia device

Camera, microphone

2007

Swaminathan et al.[37]

multimedia device

camera, microphone

2008

Zhang et al.[43]

Sensor

switch , object detection sensor

2008

Park et al [44]

sensor

computer vision, RFID

2008

Brdiczka et al [49]

Multimedia devices

Camera, microphone array, headset

2009

Kim et al [50]

sensor

PIR sensor

2009

Hussain et al [53]

sensor

Mote, RFID

2009

Earella et al [55]

mote

TmoteSky

2009

Arcelus at al[57]

sensor

Pressure sensor

2009

Lu et al [58]

sensor

Pressure,power, motion etc

2009

A sensor network is responsible to gather environmental parameter about the home. Usage of light and temperature sensors is very common in smart homes [7], [24],[25],[31]. Light sensors are normally used to measure illumination intensity of a specific location. Pressure sensors are widely used for inhabitant location detection [32], [34]. Researchers also employ RFID and PIR sensors for location identification [24],[25],[32],[33],[44],[50]. Power sensors indicate currently using devices [58]. Sensors utilize a very low data bandwidth for communication. Sensors are unreliable and very prone to noise. There should be some methods to improve accuracy rate of sensor networks.

Smart homes are equipped with medical instruments to provide health care facilities. Blood pressure, body temperate, body weight and pulse measuring equipments are frequently used to monitor patients’ health condition [8], [17], [18]. Sometimes researchers used sophisticated medical equipments like ECG, PPG etc [8], [18]. Bio signal data format varies according to the type of devices and a common data format is proposed by Yoo et al. based on MFER standers [42]. There is a problem choosing appropriate instrument which will exactly match with user requirements. Researchers select a set of equipments according to target patients and diseases.

Multimedia device have been introduced to make an interactive and user friendly home environment [31], [32], [35], [37], [44],[49]. Cameras and microphones are commonly used for data acquisition. Plasma display, headset and LCD provide a platform for information exchange. Utilizing multimedia technology is the best way to promote smart homes as it gives home a flavor of living environment to the resident. Multimedia data processing for this purpose is still in primitive stage. We need to go far away to get an effective image processing and voice reorganization systems for smart homes.

Recently motes are using in smart homes [53], [55]. Mote is a wireless node used in WSN. It gives integrated standalone predefined functionality for rapid development of sensing network. Mote is expensive and limited to some specific usage.

To enrich the intelligent system, smart home must be provided with sufficient information from sensor, cameras, microphone and other interfacing devices. Sensory information is prone to noise and misguide the information processing system with wrong output signal.

Image processing system requires more hardware resource and relatively long time to detect and reorganize human face. If the home exceeds inhabitant limit or inhabitant change his location frequently the system becomes unstable. Voice reorganization system sometimes failed to detect commands and irritating repetition of the same speech makes the user frustrated.

The sensors and devices used in smart homes are sufficient to perceive information from the resident and environment. There may be some future work for noise reduction and energy optimization. Now the main challenge is to combine the information gathered from different types of sensor networks, equipments and multimedia system.

C. Communication media and protocols

Smart homes equipments are connected through a communication network to share and exchange information. X10 and Zigbee protocols are specially designed for house hold device control. Protocols from other established technologies like Telecommunication, Internet are added to the system for grater functionality. Table 2 lists a summary of used communication media and protocols.

Table 2: Communication media used in smart Home

Author

Type

Purpose

Year

Mozer[7]

Power-line communication system

sensor network 

1998

Williams et al.[8]

Telecommunication

connecting client to the carer

1998

Barnes et. al. [9]

Telecommunication

communicates with remote telecare control center, the clients and their carers

1998

Guillén et al.[18]

ISDN

Videoconferencing

2002

Das et al[20]

X10

control home appliances

2002

Andoh et al. [28]

Ethernet protocol

monitoring equipment connectivity

2004

Perumal et al[40]

Ethernet protocol(
Simple Object Access Protocol) (SOAP)
SMS

control home appliance

2008

Yoo et al. [42]

MFER(Medical waveform description Format Encoding Rule)
Internet

communicating and storing the bio signal data,
access to the data from remote location

2008

Raad et al. [46]

Telemedicine

support in smart home

2008

Yongping et al [52]

Communication

Zigbee

2009

Chen et al.[54]

Communication

UPnP,DLNA,OSGi etc

2009

Kumar et al. [56]

Communication

X10

2009

X10 is the most popular communication protocol used in smart homes [7], [20], [56]. It uses electrical power line to transmit signals. X10 data transmission rate is 20bit/s and can address upto 256 home appliances. It uses preinstalled house wiring to transmit X10 data which is processed by X10 devices. It is very slow, limited to few command functionalities. Moreover some noise filtering device used in modern electrical equipment may filter X10 data as a noise.

Zigbee is another accepted protocol to create a wireless home network [52]. It is a low power, lower cost wireless communication protocol. Zigbee constructs a low speed wireless ad hoc network of nodes. CSMA/CD access mechanism provides reliable data transfer through the wireless medium. IEEE 802.15.4 standard specifies the physical layer and media access control of the protocol. It is a complex protocol with tight power and bandwidth constraint.

Internet is a cost effective and readily available solution for remote communication. It provides data access facilities for telehealth care centers from distance locations [42]. Sometimes Ethernet used to connect home monitoring equipments [28].Internet has a wide coverage area with a lower cost public network and it can provide high data bandwidth for multimedia data transmission. Internet is vulnerable to unauthorized access.

Telecommunication facilities are recently introduced for telehealth care support. Some researchers are using video conferencing utilizing ISDN network [18].Traditional telephone lines are used to communicate between client and carers [8], [9].SMS based system is also an effective solution to exchange information [40].

Other protocols like UPnP, DLNA, OSGi etc are also used for home appliance control [54].

Smart home employed protocols from different technologies. There are inconsistencies between data format generated from heath monitoring equipments, sensors, TCP/IP packets and telecom data format. For data storage and interoperability a common slandered of data format must be developed.

Communication protocols, services and systems also influence on the reliability of smart home. Power line communication system is vulnerable to noise generated by electric power transmission. Sometimes client lives out of telecommunication service area. Wireless communication service erupted by electromagnetic wave generated from home appliances. Internet system and Ethernet network faces packet loss problem in case of improper shielding and wiring.

Recent modern technologies are quite enough to establish a functional communication network for smart homes. Future research should be focused on cost reduction and energy conservation.

D. Algorithms and methods

The purpose of algorithm is to provide intelligence for making the home environment interactive. Location detection algorithms are derived to employ location based services and gather information about location based activities of home user. Prediction, classification and summarization algorithms add functionalities of behavior tracking and activity recognition. Table 3 gives an overall idea about used methods and algorithms.

Table 3: Algorithms and methods for home utilization

Author

Algorithm/Methods

Function

Year

Mozer [7]

Artificial Neural Network

Prediction of future states of the home environment

1998

Williams et al.[8]

Distributed intelligent system

Health monitoring from remote location

1998

Lesser et al.[11]

Multi Agent system

Simulation of agent interactions and task interactions

1999

Krumm [13][14].

Hidden Markov model, simple feature-vector averaging and neural network algorithm

To create and evaluate behavioral model

2000

Guillén et al.[18]

Videoconferencing technology

Interactive communication

2002

Noguchi et al.[19]

Summarization algorithm

To track any changes in the system

2002

Mihailidis et al.[26]

Statistics and physics based methods

face and hand tracing

2004

Isoda et al. [27]

C4.5 algorithm

Build Spatiotemporal context 

2004

Ma et al. [30]

Case-based reasoning (CBR)

Context awareness

2005

Rahal et al[34]

Bayesian Filtering methods

To determine location of the inhabitants

2007

De Silva et al[35]

Video retrieval algorithm,
audio segmentation

Behavior tracking

2007

Gopalratnam et al.[22]

Active LeZi

Next activity Prediction

2007

Vainio et al[36]

Fuzzy logic

To recognize routines and also deviations from routines. To control lighting system

2008

Zheng et al. [38]

Self-adaptive neural network

To detect and recognize activities of daily life

2008

Virone et al.[41]

Statistical predictive algorithm

To model circadian activity rhythms (CARs)

2008

Zhang et al.[43]

prediction algorithm

To predict activities of daily life(ADL)

2008

Park et al [44]

dynamic Bayesian network (DBN)

ADL recognition

2008

Rashidi et al. [45]

Frequent and Periodic Activity Miner (FPAM) algorithm

To discover frequent and periodic activity patterns

2008

Brdiczka et al.[49]

SVM

Activity reorganization

2009

Kim et al. [50]

Bayesian classifier

Location detection

2009

Lu et al.[58]

Naïve Bayes classification

Location aware activity detection

2009

Artificial Neural Network can predict future state of the home environment by observing usage pattern of home appliances [7]. It can also utilize to detect and recognize ADL of the resident [38].Modeling of human behavior is another possible application of neural network [13], [14].Neural network consume high processing power and huge storage for data processing. Vast amount of information should be used to train up the system which requires more time to get reasonable efficiency. Neural network is still popular because it does not require any previous knowledge about the home environment and resident which is very effective to design a complex system like smart homes.

C4.5 algorithm used to build spatiotemporal context of home user [27].C4.5 is a popular machine learning algorithm which used to classify data according to different data attributes for predicting the future. Smart homes researchers applied C4.5 to match the current behavior pattern of the inhabitant with a class of previous patterns to recognize the activity state. A major disadvantage of C4.5 is that it takes long CPU time and additional memory for rule sets.

Bayesian filtering methods used to determine location of the inhabitants [34], [50]. It used last known position and last sensor state to improve accuracy of location prediction Dynamic Bayesian algorithm can identify ADL utilizing a hierarchical recognition scheme [44]. Bayesian methods derived from statistical inference which make a classification of gathered information and filter according to some predefine roles. These algorithms only consider the immediate previous state to predict the future.

Fuzzy logic is effective for home appliances control [36].Instead of using only binary logic, fuzzy systems used multi-valued logic for logical reasoning. Fuzzy logic is popular for control system, not for home intelligence.

Multi agent systems are useful when there are different types of agents for different purpose and agents should incorporate each other by knowledge sharing. Each of the agents is responsible his own domain and exchange information which has a grater impact on the total system. In smart homes multi agent system is used for simulating agent interaction and task interactions [11].If we consider smart home is a collection of agents and sensors, multi agent systems are the best option to employ distributed intelligence. For interactive environment equipped with audio visual equipment we need to add more methods for multi media data processing.

Hidden Markov model (HMM) can be used to create and evaluate behavioral model [13], [14].Markov model depends on previous several states for prediction. HMM is used when some states of Markov model are missing or hidden from the information system. HMM needs to be optimized according to numbers of states and accuracy.

Image processing methods is an efficient way for human activity recognition. It can segments the skin color of digital image for face and hand tracing [26].Image processing methods seems to have a grater possibilities of adoption for future smart homes but its implementation is very complicated and still in early stage. Audiovisual retrieval and summarization system is also helpful for behavior tracking [35].

Case based reasoning (CBR) and prediction algorithms takes decision based on previous states. Context awareness, a common feature of smart environment can be achieved by CBR [30].Active LeZi and other predictive algorithms also works with previous history to predict ADL off the resident [22],[43]. These methods can not differentiate between noise and data. Resent changes in user behavior take time to reflect back to the system. Statistical predictive algorithm used to model circadian activity rhythms [41].

Frequent and periodic activity miner (FPAM) algorithm is developed by CASAS at WSA to discover frequent and periodic activity patterns [45].User can set policies and provide feedback to customize the system. SVM is also useful for activity reorganization [49].

Algorithms are used to processed information and provide services but limited to specific functions only. Total home automation with reasonable intelligence is still a dream. Algorithms are used for location detection, next event prediction, face and hand tracing, ADL reorganization and pattern classification but can not provide reliable task automation. Processing huge information generated from input devices needs advanced data processing equipments. Using only AI algorithms will never make the system automated, the system must be utilized interface technology like interactive display and voice reorganization system to know user requirement at a given moment.

Algorithms are the most challenging part for smart homes research. There are not enough algorithms for tracking multiple inhabitants in the same time. Accuracy of the algorithms is not so satisfactory to completely rely on them. Instead of aiding people for house hold task it may irritate the user alienating people from this technology. Although researchers are using various algorithms and methods for smart homes, smart homes still partially depend of human interaction to take the accurate decision.

E. Services and Utilities

Smart home technology achieved significant improvement in health care systems like patient monitoring, telemedicine and wellness monitoring. Healthy people should concern about the wellness condition their own health to take precaution before illness. Self monitoring system draws graphical wellness condition of the resident [10]. Smart homes track the user and generates alarm if any vital sign found [17].Repository and sleeping disorder assessment is another parameter for wellness measurement [15]. Smart homes can monitor physiological data [29] and able to recognize sleeping stage [28].

Smart homes technologies devoted to provide comfort and safety to the elderly [24]. The house can recognize fall, immobility and reaction incapacity [55]. Services are targeted to specific patient groups like dementia [16]. This housing facility can control night light and cooker. A recent project devoted to measure sit-to-stand duration as a parameter of wellness for the patient suffering from bone fracture [56].

Telemedicine is an alternative way to ensure health care facilities. Patient’s physiological information is monitored from remote health support center [18],[46]. The house may be equipped with videoconferencing facilities for communication between client and service provider [18]. The home can identify sleeping disorder, inactivity, abnormal temperature and appliance usage disorder of the elderly and automatically contact to the carer [9].

Effective inhabitant activity classification, recognition and modeling are important elements to implement intelligence to the house. Smart homes can model circadian activity rhythm and spatiotemporal context. It can detect [19] and classify [58] ADL and mimic these pattern [45]. Researcher refines ADL to coarse-level and fine-level [44]. The system can recognize AC, TV and lamp usage patterns [30], [43]. Human tracking, localization of sound source and lighting change can also be detected by the home monitoring system [35]. Recent projects can successfully identify up to 22 activity pattern which is a great improvement is this area [38], [39]. A project at MIT is devoted to psychological behavior study of the resident [31].

One of the major goals of smart homes is to increase comfort by reducing interaction of the inhabitant with the house hold devices. Smart homes can control basic environmental parameter of the house like light, temperature and heating according to choice and habits of the resident [7],[20],[30].Researchers added extended facilities like TV program selection, cooking recipe display and forgotten property service to the system [32]. It can rearrange the home equipment according to resident’s habit [13][14]. Resent smart homes can identify situations like introduction, presentation, aperitif, game, siesta and can control lighting and music according to that [49].

Monitoring and control of the home appliances from remote location is a popular service that can easily be provided via smart homes [40].Web based solutions are readily available for this purpose [51], [52]. Mobile devices are also able to accommodate the service [51].

Web based data repository service is introduced for biological data logging [42].Voice operated systems are replacing traditional home automation system [37]. Instead of old dumb house hold devices, intelligent appliances are being used for more functionality [54].Energy conservation is also achievable by smart homes [54].

Most of the services are applicable for the smart homes where only one resident in living. Even sometimes it is limited to single room only. Some projects are devoted to location detection, did not implemented any service [33], [34], [50], [53]. Several hypothetical smart home designs are proposed which will be quipped with almost all health care service but in practical they are still a dream [8].People are claiming their project may accommodate a lot of services but workable for only a few situations [36].

Table 4: Smart homes utilities and services

Authors

Service

Purpose

Year

Mozer et al.[7]

Comfort

Lighting, temperature and heating control

1998

Barnes et al.[9]

Telemedicine

Abnormal sleeping disorder, unexpected inactivity, uncomfortable home temperature, fridge usage disorder detection and contact to carer.

1998

Korhonen et al. [10]

Wellness monitoring

Graphical representation of Wellness condition.

1998

Krumm et al.[13],[14]

Comfort

Arrange home environment according to the resident’s desire

2000

Nishida et al. [15]

Wellness monitoring

Respiratory and sleeping disorder assessment

2000

Adlam et al.[16]

Health care

Cooker and night light control for the patient suffering dementia

2001

Virone et al.[17]

Wellness monitoring

Activity tracking and alarm generation

2002

Guillen et al.[18]

Telemedicine

Health monitoring and support form remote location

2002

Noguchi et al. [19]

Activity recognition

Detection of user activities

2002

Das et al.[20]

Comfort

Home appliance control

2002

Helel et al.[24]

Aging in place

To provide comfort and safety to the elderly

2003

Isoda et al. [27]

Activity recognition

Spatiotemporal context modeling

2004

Andoh et al.[28]

Wellness monitoring

Sleeping stage recognition

2004

Masuda et al.[29]

Wellness monitoring

Measuring heart bit rate and monitoring respiratory system

2005

Ma et al.[30]

Activity recognition 

AC, TV and lamp usage activity reasoning

2005

Intille et al. [31]

Activity monitoring

Study human interaction with the home appliance

2005

Yamazaki

Comfort

TV program selection, cooking recipe display and forgotten property service

2006

De Silva et al.[35]

Activity recognition

Human tracking, localize sound source and lighting change detection

2007

Vainio et al.[36]

Ageing in place

Lighting control

2008

Swaminathan et al.[37]

Home automation

Voice operated appliance control

2008

Zheng et al.[38],[39]

Activity recognition

Can recognize 22 distinct activities

2008

Perumal et al. [40]

Remote control

controlling and monitoring home appliance from remote location

2008

Virone et al.[41]

Activity recognition

Modeling circadian activity rhythms and their deviations

2008

Yoo et al. [42]

Data repository

Web based repository of bio signal data

2008

Zhang et al. [43]

Activity recognition

Identification of drink making activities

2008

Park et al. [44]

Activity recognition

Coarse level and fine level ADL recognition

2008

Rashidi et al. [45]

Activity recognition

Discover pattern and mimic these pattern

2008

Raad et al.[46]

Telemedicine

Support elderly and disabled

2008

Brdiczka et al. [49]

Comfort

Identification of introduction, presentation, aperitif, game and siesta

2009

Wang et al. [51]

Remote control

Appliance monitoring and control via mobile devices and computers form distance location

2009

Yongping et al. [52]

Remote control

Appliance monitoring and control through web browser from remote location

2009

Chen et al.[54]

Energy conservation

Reducing energy wastage

2009

Chen et al.[54]

Home automation

Intelligent appliance monitoring and control

2009

Earella et al. [55]

Aging in place

Fall, immobility and reaction incapacity identification

2009

Kumar et al. [56]

Health care

To measure sit-to-stand (SiSt) duration

2009

Lu et al. [58]

Activity recognition

Classification of inhabitant activities

2009

V. Conclusion

Smart home technology is still in its early stage and has failed to achieve anticipated results. Satisfying the need of user is the major challenge in smart home research and implementation. One way is providing intelligence to each home appliance and controlling them by central intelligence. And the other way is developing a single central intelligent system to control all appliances. Providing distributed intelligence to all appliances will be more effective as it removes the burden of processing huge information from the central intelligent system. Each device is responsible for its domain and share only important information with central intelligent system. The system will be eventually transformed to a multi agent system with distributed intelligence integrating smart appliances.

Future smart homes should be developed considering privacy issues and efficient security system must be employed for information protection. The infrastructure should be fully utilized to accommodate user satisfactions. Data processing and summarization should be more accurate and cost effective. User should feel comfort with all the extra equipment installed to provide extended facilities. Finally, user’s rhythm of life should be examined carefully and smart home should behave according to the user.

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