Activity Detection of Elderly People Using Smartphone Accelerometer and Machine Learning Methods

Muhammad Imran Khan; Azhar Imran; Abdul Haleem Butt; Ateeq Ur Rehman  Butt

Authors

Muhammad Imran Khan Department of Creative Technologies, Faculty of Computing & AI, Air University, Islamabad
Azhar Imran Department of Creative Technologies, Faculty of Computing & AI, Air University, Islamabad
Abdul Haleem Butt Department of Creative Technologies, Faculty of Computing & AI, Air University, Islamabad
Ateeq Ur Rehman Butt Department of Computer Science, National Textile University, Faisalabad

Keywords:

Machine Learning, Activity Detection, Elderly People, Activity Recognition, Accelerometers, Smartphones, Sensors

Abstract

Elderly activity detection is one of the significant applications in machine learning. A supportive lifestyle can help older people with their daily activities to live their lives easier. But the current system is ineffective, expensive, and impossible to implement. Efficient and cost-effective modern systems are needed to address the problems of aged people and enable them to adopt effective strategies. Though smartphones are easily accessible nowadays, thus a portable and energy-efficient system can be developed using the available resources. This paper is supposed to establish elderly people's activity detection based on available resources in terms of robustness, privacy, and cost-effectiveness. We formulated a private dataset by capturing seven activities, including working, standing, walking, and talking, etc. Furthermore, we performed various preprocessing techniques such as activity labeling, class balancing, and concerning the number of instances. The proposed system describes how to identify and classify the daily activities of older people using a smartphone accelerometer to predict future activities. Experimental results indicate that the highest accuracy rate of 93.16% has been achieved by using the J48 Decision Tree algorithm. Apart from the proposed method, we analyzed the results by using various classifiers such as Naïve Bays (NB), Random Forest (RF), and Multilayer Perceptron (MLP). In the future, various other human activities like opening and closing the door, watching TV, and sleeping can also be considered for the evaluation of the proposed model.

Full Text

Author Biography

Abdul Haleem Butt, Department of Creative Technologies, Faculty of Computing & AI, Air University, Islamabad

Dr. Abdul Haleem Butt is an Assistant Professor at Air University, Islamabad. His research interests include Image Processing, and Artificial Intelligence.

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