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• W4285742357 abstract "An increase in life expectancy is leading to an aging society, where gradual degradation in physical/cognitive health could result in fall injuries, cardiovascular, neurological/psychological problems, or a sedentary lifestyle. Sensor-based human activity recognition (HAR) is found to be a potential assistive technology in this context to improve the quality of life of an aging society. This chapter presents the state of the art and future trends of Internet of Things (IoT)-enabled sensor data-based Human Activity Recognition (HAR) systems, where machine learning (ML)/deep learning (DL) is used to classify human actions in the context of healthcare applications. In particular, the impact of IoT on the healthcare industry is so immense that a dedicated specialization, termed as Internet of Healthcare Things (IoHT), has emerged. Smart healthcare systems are equipped with different categories of sensors (wearable, wireless, and ambient). The huge amount of sensor data acquired by IoHT sensors allows for performing accurate HAR. The HAR involves detection/classification problems, e.g. fall detection and seizer detection, where ML is used extensively, at its heart. A lot of ML/DL techniques are being used in HAR including support vector machine (SVM), artificial neural network (ANN), recurrent neural network (RNN), convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTM), etc. However, there are a few special aspects of HAR sensor data that make it difficult to handle. Involved research is still being pursued across the globe to solve these problems." @default.
• W4285742357 created "2022-07-18" @default.
• W4285742357 creator A5037941914 @default.
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• W4285742357 date "2022-01-01" @default.
• W4285742357 modified "2023-09-24" @default.
• W4285742357 title "Human Activity Recognition Systems Based on Sensor Data Using Machine Learning" @default.
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• W4285742357 doi "https://doi.org/10.1007/978-981-19-1408-9_6" @default.
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