Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

Abstract

Human activity recognition (HAR) is a classification process that is used for recognizing human motions. A comprehensive review of currently considered approaches in each stage of HAR, as well as the influence of each HAR stage on energy consumption and latency is presented in this paper. It highlights various methods for the optimization of energy consumption and latency in each stage of HAR that has been used in literature and was analyzed in order to provide direction for the implementation of HAR in health and wellbeing applications. This paper analyses if and how each stage of the HAR process affects energy consumption and latency. It shows that data collection and filtering and data segmentation and classification stand out as key stages in achieving a balance between energy consumption and latency. Since latency is only critical for real-time HAR applications, the energy consumption of sensors and devices stands out as a key challenge for HAR implementation in health and wellbeing applications. Most of the approaches in overcoming challenges related to HAR implementation take place in the data collection, filtering and classification stages, while the data segmentation stage needs further exploration. Finally, this paper recommends a balance between energy consumption and latency for HAR in health and wellbeing applications, which takes into account the context and health of the target population.

Effect of Equipment on the Accuracy of Accelerometer-Based Human Activity Recognition in Extreme Environments

“…The need to optimise the accuracy and performance of HAR systems is well established. A large body of the work within the sensor-based HAR field focuses on feature extraction [ 14 , 15 ] and the development and implementation of classification models [ 16 ] to improve recognition accuracy. Whilst these aspects are unarguably essential to improve the performance of HAR systems, they often overlook some of the elements which are most influential in real-world applications.…”

Orientation-Independent Human Activity Recognition Using Complementary Radio Frequency Sensing

“…Therefore, HAR and fall detection systems are the essential building blocks of any robust AAL system. In addition, robust HAR is important for properly monitoring and quantifying physical activities, which can contribute to various smart-health [2] and wellbeing applications [3,4].…”

A New Method for Human Activity Recognition of Photoplethysmography Signals Using Wavelet Scattering Transform

“…Humans' physical activity condition is a reflection of their physical health. By collecting and analyzing HAR data, can help people live a healthier lifestyle and prevent various chronic diseases [1]. So HAR has enormous development potential in several fields such as health care and monitorization of patient.…”