Human Activity Recognition Based on Multi-Sensors in a Smart Home Using Deep Learning

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Aqel Musbah; Sokiyna Munsif

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Journal Paper

Paper Information

Journal: THE ISC INTERNATIONAL JOURNAL OF INFORMATION SECURITY Year:2021 | Volume:13 | Issue:3 Page(s): 69-78

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Title

Human Activity Recognition Based on Multi-Sensors in a Smart Home Using Deep Learning

Author(s)

Aqel Musbah | Sokiyna Munsif | Issue Writer Certificate

Keywords

ADL

SVM

LSTM

1-Dcnn

Sliding Window Preprocessing

Convolutional Neural Network

Recurrent Neural Network

Abstract

Tracking or taking care of elderly people when they live alone is a much more challenging area. Because most aged people su er from some health issues like Alzheimer's, diabetes, and hypertension, in case happening any abnormal activity or any emergency since they live alone and there is no one around them to o er any support, one of the best choices to care mature people is focusing on smart home technology. Also, one of the essential keys to expanding smart home technology is monitoring, detecting, and recognizing human activities called Ambient Assisted Living (AAL) applications. Nowadays our world highly focuses on a smart system because the smart system can learn habits, and if it nds any problem or any abnormal happenings, it can take automated decisions for residents for example, by learning cooking time, the system can prepare the oven, and by learning spare time which the resident spend for watching, the system can prepare the TV also put it to a favorite channel for the residents. To do this, a new and existing established machine learning and deep learning approaches are required to be estimated the system focusing on using real datasets. So, this study presents machine learning to analyze activities of daily living (ADL) in smart home environments. The data sets were collected from a set of binary sensors installed on two houses. This study used public data sets for detecting and recognizing human activities, the data set was tested based on machine learning classi cation especially Support Vector Machines (SVM) was applied as the traditional neural network also for deep learning (1-Dcnn) as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) as Recurrent Neural Network (RNN) and was used. Also, sliding window (windowing) was used in the preprocessing phase, the study concludes that all used algorithms can detect some activities perfectly, and on the other hand they can't predict all activities perfectly especially those activities that take short-time, the main key for this situation is imbalanced data.

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APA: Copy

Aqel, Musbah, & Sokiyna, Munsif. (2021). Human Activity Recognition Based on Multi-Sensors in a Smart Home Using Deep Learning. THE ISC INTERNATIONAL JOURNAL OF INFORMATION SECURITY, 13(3), 69-78. SID. https://sid.ir/paper/974690/en

Vancouver: Copy

Aqel Musbah, Sokiyna Munsif. Human Activity Recognition Based on Multi-Sensors in a Smart Home Using Deep Learning. THE ISC INTERNATIONAL JOURNAL OF INFORMATION SECURITY[Internet]. 2021;13(3):69-78. Available from: https://sid.ir/paper/974690/en

IEEE: Copy

Musbah Aqel, and Munsif Sokiyna, “Human Activity Recognition Based on Multi-Sensors in a Smart Home Using Deep Learning,” THE ISC INTERNATIONAL JOURNAL OF INFORMATION SECURITY, vol. 13, no. 3, pp. 69–78, 2021, [Online]. Available: https://sid.ir/paper/974690/en

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