Energy efficient human activity recognition using wearable sensors

Ding, Genming; Tian, Junfang; Wu, Jinsong; Zhao, Qian; Xie, Lili

doi:10.1109/wcncw.2018.8368980

Cited by 22 publications

(22 citation statements)

References 10 publications

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“…Gordon et al [19] tried to save energy by turning off the irrelevant sensors during the activity recognition phase. Ding et al [20] improved energy efficiency by reducing the computational complexity during the feature selection in the time and frequency domains. Razzaque et al [21] achieved energy-efficiency by using compressed sensing.…”

Section: Energy-efficient Activity Recognitionmentioning

confidence: 99%

An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition

Chen

Huang

2020

Sensors

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In recent years, sensors in smartphones have been widely used in applications, e.g., human activity recognition (HAR). However, the power of smartphone constrains the applications of HAR due to the computations. To combat it, energy efficiency should be considered in the applications of HAR with smartphones. In this paper, we improve energy efficiency for smartphones by adaptively controlling the sampling rate of the sensors during HAR. We collect the sensor samples, depending on the activity changing, based on the magnitude of acceleration. Besides that, we use linear discriminant analysis (LDA) to select the feature and machine learning methods for activity classification. Our method is verified on the UCI (University of California, Irvine) dataset; and it achieves an overall 56.39% of energy saving and the recognition accuracy of 99.58% during the HAR applications with smartphone.

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Section: Energy-efficient Activity Recognitionmentioning

confidence: 99%

An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition

Chen

Huang

2020

Sensors

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“…Energy consumption is a crucial factor for certain applications of activity recognition, such as the long term monitoring of patients in health and wellbeing [8]. Furthermore, power efficiency along with computational efficiency appears to be the main challenge for wearable device-based HAR implementation [10]. Communications, sensing and computation tasks are generally the sources of energy consumption in HAR [8].…”

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

confidence: 99%

“…Feature extraction derivates various and broad features that are distinguishing for activities [62]. Deep learning methods, such as Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) can be used for feature extraction [10]. These methods do not require expert knowledge [68].…”

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

confidence: 99%

“…The reduction in the number of sensors can help improve the energy efficiency of the sensor device [61], whilst adding new sensor-type events can improve accuracy [54]. The number of sensors in this stage also affects the ability for complex activity detection, which is easier done with more than a single sensor unit [10]. In health and wellbeing applications, new sensor types (especially wearables) can be impractical for elderly people [52] because they are a source of discomfort for them.…”

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

confidence: 99%

“…Furthermore, in the case of non-contact sensing, the number of sensors depends of their type and the covered HAR area. Having this in mind, some authors measured energy efficiency of HAR approaches with wearables [8,10,14,30,31,32]. Some authors analyzed the energy consumption of activity recognition of smartphones [45,111,112].…”

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

confidence: 99%

See 2 more Smart Citations

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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LSTM-Based Deep Learning Architecture for Recognition of Human Activities

Kallepally

Rajesh

2023

Advances in Intelligent Systems and Computing

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Energy efficient human activity recognition using wearable sensors

Cited by 22 publications

References 10 publications

An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition

An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition

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

LSTM-Based Deep Learning Architecture for Recognition of Human Activities

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