Smart home based ambient assisted living: Recognition of anomaly in the activity of daily living for an elderly living alone

Ghayvat, Hemant; Mukhopadhyay, Subhas Chandra; Shenjie, B.; Chouhan, Arpita; Chen, Wei

doi:10.1109/i2mtc.2018.8409885

Cited by 57 publications

(47 citation statements)

References 9 publications

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“…They used sparse learning based classification techniques in association with clustering approaches. Chen, Hung et al [21] worked on activity detection of elderly citizens by performing the categorization at the sensor level. The categorization of the sensors is performed according to daily activities.…”

Section: Related Workmentioning

confidence: 99%

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A Unified Framework for Behaviour Monitoring and Abnormality Detection for Smart Home

Grewal

Kaur

Park

2019

Wireless Communications and Mobile Computing

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With the revolution in smart infrastructure in the recent past, the smart healthcare system has been paid more considerable attention. The continuous upgradation of electricity meters to smart electricity devices has probed into a new market of intelligent data analysis services, providing aid to the health care systems. This paper presents a unified framework for extracting user behaviour patterns from home-based smart electricity meter data. The structure allows exploration and integration of frequent pattern growth algorithm for pattern mining and application of a variety of machine learning algorithms for categorizing the activities into manually labelled classes along with the implementation of Local Outlier Factor method for detection of an abnormal pattern of the inhabitant of smart homes. To evaluate the proposed framework, the work is implemented on the smart electricity dataset from the United Kingdom by separating the data into four distinct data files meant for the morning, afternoon, evening, and night energy utilization records. The results show a remarkable performance of Support Vector Machine (SVM) and Multilayer Perceptron (MLP) classifiers with kappa statics greater than 0.95 for all time slots data. The resultant frequent device utilization patterns with anomaly score more than the threshold value, reflecting abnormal activity patterns, are found more in evening time data in comparison to other time slots, requiring the immediate attention of concerned healthcare authorities.

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Section: Related Workmentioning

confidence: 99%

“…Different methods for multiple resident activity detection are evaluated on the same data. Ghaywat et al [31] proposed a real-time approach to healthcare monitoring in real time where the activities of daily life are predicted based on past data. The work employed time series analysis techniques to detect normal and abnormal activities.…”

Section: Related Workmentioning

confidence: 99%

A Unified Framework for Behaviour Monitoring and Abnormality Detection for Smart Home

Grewal

Kaur

Park

2019

Wireless Communications and Mobile Computing

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“… E-health System: To improve the quality of life, smart microgrid can be used to manage health-monitoring system based on smart meter and load profiling. By using the information such as energy consumption, appliances' usage information and time duration of using appliances, the independent living of elderly or patients with selflimiting health conditions can be regularly monitored by caregivers [20] [21].…”

Section: Innovationmentioning

confidence: 99%

Creation of CERID: Challenge, Education, Research, Innovation, and Deployment “In the Context of Smart MicroGrid”

Ebrahimi

Kelati

Nkonoki

et al. 2019

2019 IST-Africa Week Conference (IST-Africa)

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The iGrid project deals with the design and implementation of a solarpowered smart microgrid to supply electric power to small rural communities. In this paper, we discuss the roadmap of the iGrid project, which forms by merging the roadmaps of KIC (knowledge and Innovation Community) and CDE (Challenge-Driven Education). We introduce and explain a five-gear chain as Challenge, Education, Research, Innovation, and Deployment, called CERID, to reach the main goals of this project. We investigate the full chain in the iGrid project, which is established between KTH Royal Institute of Technology (Sweden) and University of Dar es Salam (Tanzania). We introduce the key stakeholders and explain how CERID goals can be accomplished in higher educations and through scientific research. Challenges are discussed, some innovative ideas are introduced and deployment solutions are recommended.

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“…The ability of an older adult to perform his or her activities of daily living (ADLs) represents his functional capacity (Ghayvat, Mukhopadhyay, Shenjie, Chouhan, & Chen, ). ADL is a tool used to assess the older adults' abilities to navigate daily life and achieve needed objectives; this type of assessment can be particularly helpful when considering the context of cognitive impairment (Zhang, Song, & Xue, ).…”

Section: Introductionmentioning

confidence: 99%

Situation‐centered goal reinforcement of activities of daily living in smart home environments

2019

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Older adults with early‐stage dementia (ED) can experience confusion or lack clarity when performing routine activities of daily living (ADLs). These circumstances predispose the older adult to safety‐critical and often risky situations. A safety‐critical risky situation is one that constitutes a hazard. To support independent living, a sensor‐laden smart environment can be employed to mitigate such hazards. In this paper, we propose a situation‐centered goal reinforcement framework that supports older adults with ED in their decision making, and guides them through their ADL in order to fulfill their goal or intention and avoid hazards. First, we employ an LSTM (Long Short‐Term Memory) model to infer the current goal of the resident, using their previously observed normal ADL patterns. Secondly, we identify potentially risky situations in their currently observed goal path. We then incorporate a situ‐learning agent (SLA) that helps an inhabitant to make the right decision, thus preventing adverse events while guiding her through the task sequence that leads to her goal state. In addition, we use a naïve agent to simulate episodes of confusion similar to those that might be experienced by older adults with ED. We validated our method against an open‐source dementia dataset (Quesada et al., 2015) by considering four types of ADLs as case studies. We achieved an accuracy of 90.1% for our goal inference model, higher than the accuracies reported by related studies. We also reported other metrics including precision, recall and f1‐score for goal inference model. Finally, SLA's action recommendations relevance was evaluated accordingly.

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Smart home based ambient assisted living: Recognition of anomaly in the activity of daily living for an elderly living alone

Cited by 57 publications

References 9 publications

A Unified Framework for Behaviour Monitoring and Abnormality Detection for Smart Home

A Unified Framework for Behaviour Monitoring and Abnormality Detection for Smart Home

Creation of CERID: Challenge, Education, Research, Innovation, and Deployment “In the Context of Smart MicroGrid”

Situation‐centered goal reinforcement of activities of daily living in smart home environments

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