Human Activity Detection using Profound Learning with Improved Convolutional Neural Networks
Keywords:
Human Activity Detections, Deep Leaning Techniques, Convolution Neural Networks, Machine learning Techniques, healthcare monitoring.Abstract
Human Activity recognition (HAR) is an interesting area of research mainly due to the availability of low cost sensors and accelerometers live streaming of data and advances in technology. HARs involve identifying various human activities such as walking, running, sitting, sleeping, standing, showering, cooking, driving, opening the door, abnormal activities, etc. are recognized. The data can be collected from wearable sensors or accelerometer. HARs can be extensively used in medical diagnostics for keeping track of elderly people, HARs approaches analyze data acquired from sensing devices, including vision and embedded sensors. HARs are assistive technologies mainly used for taking care of elders in healthcare. Approaches of HARs attempt to predict people’s movements often indoors and based on sensor data like accelerometers of smart phones. In terms of classifications, HARs are challenging tasks as they involve time series data where Deep Learning Techniques (DLTs) like CNNs (Convolution Neural Networks) have the ability to correctly engineer features from these raw data while building their learning models. This paper proposes Human Activity Detections using Profound Learning (HADPL) based on CNNs which detects HARs from captured accelerometer data. HADPL was tested on WISDM_Act_v1.1 dataset and evaluated for its performances in terms of precisions, accuracies, recalls and F1-scores where it achieved a decent level of accuracy by scoring up to 95 percent. The proposed technique can be implemented for monitoring elderly people based on captured or stored HAR data.
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