Flexible and Wearable EMG and PSD Sensors Enabled Locomotion Mode Recognition for IoHT-Based In-Home Rehabilitation

Zhao, Yifan; Wang, Jiaxin; Zhang, Yifei; Liu, Hejian; Chen, Ziang; Lü, Yanwu; Dai, Yanning; Xu, Lijun; Gao, Shuo

doi:10.1109/jsen.2021.3058429

Cited by 32 publications

(25 citation statements)

References 64 publications

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“…Sensors generate data by sensing physical parameters from their environment, e.g., temperature, pressure, etc. [49], ventilators, and ECG machines installed in medical labs and diagnostics rooms. These machines are not mobile because of their sizeable physical dimension.…”

Section: Device Layermentioning

confidence: 99%

Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)

et al. 2022

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The Internet of Things (IoT) is an emerging field consisting of Internet-based globally connected network architecture. A subset of IoT is the Internet of Healthcare Things (IoHT) that consists of smart healthcare devices having significant importance in monitoring, processing, storing, and transmitting sensitive information. It is experiencing novel challenges regarding data privacy protection. This article discusses different components of IoHT and categorizes various healthcare devices based on their functionality and deployment. This article highlights the possible points and reasons for data leakage, such as conflicts in laws, the use of sub-standard devices, lack of awareness, and the non-availability of dedicated local law enforcement agencies. This article draws attention to the escalating demand for a suitable regulatory framework and analyzes compliance problems of IoHT devices concerning healthcare data privacy and protection regulations. Furthermore, the article provides some recommendations to improve the security and privacy of IoHT implementation.

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Section: Device Layermentioning

confidence: 99%

Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)

et al. 2022

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“…In [ 37 ], the locomotion mode recognition using wearable sensors is demonstrated. The models show an intelligent perception by employing adaptable electromyography (EMG) sensors and a forty-eight-channel plantar stress distribution sensor for locomotion mode recognition.…”

Section: Related Workmentioning

confidence: 99%

FL-PMI: Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems

Arikumar

Prathiba

Alazab

et al. 2022

Sensors

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Recent technological developments, such as the Internet of Things (IoT), artificial intelligence, edge, and cloud computing, have paved the way in transforming traditional healthcare systems into smart healthcare (SHC) systems. SHC escalates healthcare management with increased efficiency, convenience, and personalization, via use of wearable devices and connectivity, to access information with rapid responses. Wearable devices are equipped with multiple sensors to identify a person’s movements. The unlabeled data acquired from these sensors are directly trained in the cloud servers, which require vast memory and high computational costs. To overcome this limitation in SHC, we propose a federated learning-based person movement identification (FL-PMI). The deep reinforcement learning (DRL) framework is leveraged in FL-PMI for auto-labeling the unlabeled data. The data are then trained using federated learning (FL), in which the edge servers allow the parameters alone to pass on the cloud, rather than passing vast amounts of sensor data. Finally, the bidirectional long short-term memory (BiLSTM) in FL-PMI classifies the data for various processes associated with the SHC. The simulation results proved the efficiency of FL-PMI, with 99.67% accuracy scores, minimized memory usage and computational costs, and reduced transmission data by 36.73%.

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“…In the study by Zhao et al, [ 19 ] a multisensory fusion‐based locomotion–recognition technique was presented. The authors developed a pair of insole force‐sensing systems (48 channels for each foot) to read plantar stress distribution as well as an electromyography EMG‐based intention detection system.…”

Section: Wearables and Implant‐based Human Body Signal Retrievingmentioning

confidence: 99%

Advanced Electronics and Artificial Intelligence: Must‐Have Technologies Toward Human Body Digital Twins

Dai

Wang

Gao

2022

Advanced Intelligent Systems

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The development of precise virtual entities of human bodies has been pursued for many years, but to date, there have been few positive reports. In recent years, the success of digital twin (DT) technology in industrial applications has led to greater interest in establishing human‐body DT models. In particular, many recent advances in materials science, electrical engineering, and computer engineering have exhibited the intrinsic power of DT models in the detection and processing of complex body signals, indicating that the goals of establishing human‐body DTs are getting “within reach.” Herein, current DT technologies in industry are discussed and advanced electronics and artificial intelligent algorithms are reviewed, which are must‐have building blocks for human DTs. Finally, perspectives on the use of human‐body DTs in improving the quality of life and introduction of never‐have immersive virtual reality experiences are discussed.

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Flexible and Wearable EMG and PSD Sensors Enabled Locomotion Mode Recognition for IoHT-Based In-Home Rehabilitation

Cited by 32 publications

References 64 publications

Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)

Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)

FL-PMI: Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems

Advanced Electronics and Artificial Intelligence: Must‐Have Technologies Toward Human Body Digital Twins

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