Elderly activities recognition and classification for applications in assisted living

Chernbumroong, Saisakul; Cang, Shuang; Atkins, Anthony; Yu, Hongnian

doi:10.1016/j.eswa.2012.09.004

Cited by 291 publications

(193 citation statements)

References 26 publications

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“…A variety of on-body sensors have been explored such as accelerometer [5,7,8,9,10,11], gyroscope [6,11], temperature [6,7,9], etc.…”

Section: Related Workmentioning

confidence: 99%

“…A number of studies use several sensors attached to different parts of human body to increase recognition accuracy. Locations such as chest [10,11], wrist [5,6,7,11], thigh [10], waist [12], ankle [10,11], etc. have been studied.…”

Section: Related Workmentioning

confidence: 99%

“…Due to this reason, a non-visual based activity recognition approach is more suitable. Recently, non-visual based activity recognition [5,6] have been studied in an attempt of proposing a model that is practical and highly accurate.…”

Section: Introductionmentioning

confidence: 99%

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A practical multi-sensor activity recognition system for home-based care

Chernbumroong

Cang

2014

Decision Support Systems

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To cope with the increasing number of ageing population, a type of care which can help prevent or postpone entry into institutional care is preferable. Activity recognition can be used for home-based care in order to help elderly people to remain at home as long as possible. This paper proposes a practical multi-sensor activity recognition system for home-based care utilizing on-body sensors. Seven types of sensors are investigated on their contributions toward activity classification. We collected a real data set through the experiments participated by a group of elderly people. Seven classification models are developed to explore contribution of each sensor.We conduct a comparison study of four feature selection techniques using the developed models and the collected data. The experimental results show our proposed system is superior to previous works achieving 97% accuracy.The study also demonstrates how the developed activity recognition model can be applied to promote a home-based care and enhance decision support system in health care.

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“…A variety of on-body sensors have been explored such as accelerometer [5,7,8,9,10,11], gyroscope [6,11], temperature [6,7,9], etc.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A practical multi-sensor activity recognition system for home-based care

Chernbumroong

Cang

2014

Decision Support Systems

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“…Human activity recognition in natural settings is an active research area that has been applied widely in the field of chronic disease management and rehabilitation [15]- [17]. It is also motivated by a wide range of mobile and ubiquitous computing applications which include personalisation of user interfaces [10], further aided by the development of inertial sensors.…”

Section: Introductionmentioning

confidence: 99%

“…Machines (SVM) [22], [25], Decision Trees (DT) [9], [16], Naive Bayes (NB) [16], Multi-Layer Perceptron (MLP) [17], Artificial Neural Networks (ANN) [9], or a combination of these techniques [15]. Hidden Markov Models (HMM) [10] have been used for recognising common gestures made when interacting with objects used in daily living.…”

Section: Introductionmentioning

confidence: 99%

Recognition of elementary arm movements using orientation of a tri-axial accelerometer located near the wrist

et al. 2014

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Abstract:In this paper we present a method for recognising three fundamental movements of the human arm (reach and retrieve, lift cup to mouth, rotation of the arm) by determining the orientation of a tri-axial accelerometer located near the wrist. Our objective is to detect the occurrence of such movements performed with the impaired arm of a stroke patient during normal daily activities as a means to assess their rehabilitation. The method relies on accurately mapping transitions of predefined, standard orientations of the accelerometer to corresponding elementary arm movements. To evaluate the technique, kinematic data was collected from four healthy subjects and four stroke patients as they performed a number of activities involved in a representative activity of daily living, 'making-a-cup-of-tea'. Our experimental results show that the proposed method can independently recognise all three of the elementary upper limb movements investigated with accuracies in the range 91%-99% for healthy subjects and 70%-85% for stroke patients.

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A home security system for seniors based on the beacon technology

Tsai

2018

Concurrency and Computation

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Summary Many elderly people have a hard time adapting to information technology (IT); for instance, many wearable devices have such rich functions that render them highly power‐consuming, creating operational difficulties for the elderly. The wearable devices must constantly be recharged, which is also an issue for disabled elderly people. Hence, this study utilizes transmitter and receiver of the beacon to learn about the habits of elderly people in their residence while monitoring their heart rate to track their physical condition; when and if they press the medical alarm, help can be sent on their way. This study uses the beacon device to position the location of elderly people in their residence. We have developed a wearable wristband that transmits data through the beacon. The only button on the wristband is a medical alarm; other functions include home positioning and heart rate monitoring, which operate automatically to relieve the elderly people from information technology adaptation issues. This paper will also develop an integrative monitoring platform that aims at detecting whether an elderly person has pressed the medical alarm as well as analyzing whether their heart rate is regular. Given that elderly people stay mostly within the range of their residence, the platform will conduct statistical analysis on schedule of the elderly person of being in and out of the house; if they have left their house for too long or have displayed irregular behavior in leaving or entering the house, then their family will be notified. The proposed method has added privacy and access authority control to prevent malicious parties from tracking the schedule or the residence of elderly people from being invaded. This study has also completed application development and conducted privacy efficacy analysis.

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Elderly activities recognition and classification for applications in assisted living

Cited by 291 publications

References 26 publications

A practical multi-sensor activity recognition system for home-based care

A practical multi-sensor activity recognition system for home-based care

Recognition of elementary arm movements using orientation of a tri-axial accelerometer located near the wrist

A home security system for seniors based on the beacon technology

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