Feature Grouping Based On Ga And L-Gem For Human Activity Recognition

Xue, Yi-Wen; Liu, Jing; Chen, Jiamin; Zhang, Yuntao; Cao, Renhua

doi:10.1109/icmlc.2018.8527017

Cited by 2 publications

(1 citation statement)

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“…Since Convolution Neural Networks (CNN) combine feature extraction and classification in an end-to-end approach [37], they can perform classification in a very efficient way [55,89]. Recurrent Neural Networks (RNN), however, outperformed CNN for short duration activities.…”

Section: Impact Of Human Activity Recognition (Har) Stages On Enermentioning

confidence: 99%

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

Dinarevic

Husić

Baraković

2019

Sensors

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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.

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Section: Impact Of Human Activity Recognition (Har) Stages On Enermentioning

confidence: 99%