LoRa Posture Recognition System Based on Multi-Source Information Fusion

Feng, Ning; Song, Wei; Han, Jinkun; Xie, Junwei; Gao, Yanning; Song, Liangliang

doi:10.1109/ithings/greencom/cpscom/smartdata.2019.00160

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Wang et al collected four swimming style data through inertial sensors arranged at the waist, based on the HMM extracted the data fusion information with high recognition rate 36 . In 2019, Feng et al realized multisource information fusion through a multivariate long range system, completed smooth filtering and data denoising by preprocessing sensor data and feature extraction, used sliding windows for stream segmentation and frequency domain feature extraction, and constructed an MRMR‐SFS‐RF‐based pose recognition model, which experimentally proved that in a small number of identification accuracies in the data was 98.9% 37 . Zadghorban established a posture classification model based on myoelectricity sensors through a multisensor wearable device, placing eight‐channel SEMG sensors equidistantly on the forearm and using filters to remove irrelevant features, but the drawback is that the accuracy of sensor data classification depends largely on the precision of placement 38 .…”

Section: Related Workmentioning

confidence: 99%

“…36 In 2019, Feng et al realized multisource information fusion through a multivariate long range system, completed smooth filtering and data denoising by preprocessing sensor data and feature extraction, used sliding windows for stream segmentation and frequency domain feature extraction, and constructed an MRMR-SFS-RF-based pose recognition model, which experimentally proved that in a small number of identification accuracies in the data was 98.9%. 37 Zadghorban established a posture classification model based on myoelectricity sensors through a multisensor wearable device, placing eightchannel SEMG sensors equidistantly on the forearm and using filters to remove irrelevant features, but the drawback is that the accuracy of sensor data classification depends largely on the precision of placement. 38 Rafael et al established human activity recognition through wearable multisensor insoles classifier, which is an ultrasonic sensor to detect lower limb motion in an unsupervised environment, but also requires accurate sensor placement during data acquisition to determine the orientation of each sensor and attachment location in advance.…”

Section: Sensor-based Gesture Recognitionmentioning

confidence: 99%

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A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment

Liu

Jiang

et al. 2021

Int J Intell Syst

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Stroke is one of the leading causes of death and disability in the world. The rehabilitation of Patients' limb functions has great medical value, for example, the therapy of functional electrical stimulation (FES) systems, but suffers from effective rehabilitation evaluation. In this paper, six gestures of upper limb rehabilitation were monitored and collected using microelectromechanical systems sensors, where data stability was guaranteed using data preprocessing methods, that is, deweighting, interpolation, and feature extraction. A fully connected neural network has been proposed investigating the effects of different hidden layers, and determining its activation functions and optimizers. Experiments have depicted that a three‐hidden‐layer model with a softmax function and an adaptive gradient descent optimizer can reach an average gesture recognition rate of 97.19%. A stop mechanism has been used via recognition of dangerous gesture to ensure the safety of the system, and the lightweight cryptography has been used via hash to ensure the security of the system. Comparison to the classification models, for example, k‐nearest neighbor, logistic regression, and other random gradient descent algorithms, was conducted to verify the outperformance in recognition of upper limb gesture data. This study also provides an approach to creating health profiles based on large‐scale rehabilitation data and therefore consequent diagnosis of the effects of FES rehabilitation.

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Section: Related Workmentioning

confidence: 99%

Section: Sensor-based Gesture Recognitionmentioning

confidence: 99%

A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment

Liu

Jiang

et al. 2021

Int J Intell Syst

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An Intelligent Method for Upper Limb Posture Recognition Based on Limited MEMS Data

Liu

et al. 2021

2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf

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Sustainable Technologies for Older Adults

et al. 2021

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The exponential evolution of technology and the growth of the elderly population are two phenomena that will inevitably interact with increasing frequency in the future. This paper analyses scientific literature as a means of furthering progress in sustainable technology for senior living. We carried out a bibliometric analysis of papers published in this area and compiled by the Web of Science (WOS) and Scopus, examining the main participants and advances in the field from 2000 to the first quarter of 2021. The study describes some interesting research projects addressing three different aspects of older adults’ daily lives—health, daily activities and wellbeing—and policies to promote healthy aging and improve the sustainability of the healthcare system. It also looks at lines of research into transversal characteristics of technology. Our analysis showed that publications mentioning sustainability technologies for older adults have been growing progressively since the 2000s, but that the big increase in the number of research works in this area took place during the period 2016–2021. These more recent works show a tendency to study those factors that improve healthy aging, ensure the social inclusion of the elderly through technology and prolong the time in which they can live independent lives thanks to smart environments. Current research gaps in the literature are also discussed.

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LoRa Posture Recognition System Based on Multi-Source Information Fusion

Cited by 3 publications

References 6 publications

A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment

A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment

An Intelligent Method for Upper Limb Posture Recognition Based on Limited MEMS Data

Sustainable Technologies for Older Adults

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