scholarly journals Adaptive Extreme Edge Computing for Wearable Devices

2021 ◽  
Vol 15 ◽  
Author(s):  
Erika Covi ◽  
Elisa Donati ◽  
Xiangpeng Liang ◽  
David Kappel ◽  
Hadi Heidari ◽  
...  
Keyword(s):  
Power Consumption ◽  
Wearable Sensors ◽  
Wearable Devices ◽  
Distributed Networks ◽  
Edge Computing ◽  
Neuromorphic Computing ◽  
Oxide Semiconductors ◽  
Holistic View ◽  
Extreme Edge ◽  
Smart Wearable

Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g., memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.

scholarly journals Understanding Smart Wearable Sensors Technology: Impact on Human Health and Fitness

2020 ◽  
Vol 24 (7) ◽  
pp. 1261-1265
Author(s):  
E.P. Idoga ◽  
A.I. Adamu
Keyword(s):  
Human Health ◽  
Wearable Sensors ◽  
Wearable Devices ◽  
Vital Role ◽  
Healthcare Sector ◽  
Web Based ◽  
Healthcare Needs ◽  
Personal Motivation ◽  
Health And Fitness ◽  
Smart Wearable

In the field of health care management, smart wearable devices and its supporting technologies have tremendously made a name all around the globe. Smart watches and other sensor trackers are practically being used by various people and its usage has shown to be accompanied with lots  of benefits. This technology was envisaged to play a vital role in the healthcare needs of people; especially with applications in the healthcare sector. The objective of this study, therefore, is to evaluate the technological impact of wearable sensors in human health and fitness (HHF). A web based survey was used for data collection for the period of one month. Emails were sent to registered members of a particular gym who uses any of the smart wearable sensors in keeping fit. The study findings indicate that among the smart wearable devices examined, smart wristwatches (45.6%) appears to be the most commonly used wearable sensor device followed by smart wrist bands (34.7%), smart textiles (10.7%) and smart rings (9.1%). This signifies that a large number of people can effortlessly use SWSs and devices and are optimistic about its support in their daily  healthcare/fitness needs. Users are positive on the technological prospects of SWSs and devices; although there is a gap between personal  motivation to use wearable devices and trust in the confidentiality and privacy of data generated. Keywords: Devices, Health, Fitness, Wearable, Sensors

scholarly journals A Review of Human Motion Monitoring Methods using Wearable Sensors

2018 ◽  
Vol 14 (10) ◽  
pp. 168 ◽  
Author(s):  
Yaqing Tu ◽  
Linfeng Liu ◽  
Ming Li ◽  
Peng Chen ◽  
Yuwen Mao
Keyword(s):  
Wearable Sensors ◽  
Wearable Devices ◽  
Human Motion ◽  
Font Size ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Existing Problems ◽  
Acceleration Sensors ◽  
Human Motion Monitoring ◽  
Smart Wearable

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: DE; mso-ansi-language: EN-US; mso-bidi-language: AR-SA;">Human motion monitoring with wearable devices has become a hot research topic in the field of smart wearable devices. In this paper, the principles and historical development of human motion monitoring with wearable devices are analyzed and reviewed, respectively. Specifically, the current situation of human motion monitoring with wearable devices based on acceleration sensors is analyzed first. Then the existing problems and the future developing trends of research on human motion monitoring methods are summarized. Finally, the prospects of the research on of human motion monitoring method with wearable devices are discussed.</span>

scholarly journals Smart Wearable Sensors Based on Triboelectric Nanogenerator for Personal Healthcare Monitoring

Micromachines ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 352
Author(s):  
Ruonan Li ◽  
Xuelian Wei ◽  
Jiahui Xu ◽  
Junhuan Chen ◽  
Bin Li ◽  
...  
Keyword(s):  
Medical Care ◽  
Health Monitoring ◽  
Wearable Sensors ◽  
The Elderly ◽  
The Body ◽  
Assessment System ◽  
Triboelectric Nanogenerator ◽  
Personal Healthcare ◽  
Healthcare Monitoring ◽  
Smart Wearable

Accurate monitoring of motion and sleep states is critical for human health assessment, especially for a healthy life, early diagnosis of diseases, and medical care. In this work, a smart wearable sensor (SWS) based on a dual-channel triboelectric nanogenerator was presented for a real-time health monitoring system. The SWS can be worn on wrists, ankles, shoes, or other parts of the body and cloth, converting mechanical triggers into electrical output. By analyzing these signals, the SWS can precisely and constantly monitor and distinguish various motion states, including stepping, walking, running, and jumping. Based on the SWS, a fall-down alarm system and a sleep quality assessment system were constructed to provide personal healthcare monitoring and alert family members or doctors via communication devices. It is important for the healthy growth of the young and special patient groups, as well as for the health monitoring and medical care of the elderly and recovered patients. This work aimed to broaden the paths for remote biological movement status analysis and provide diversified perspectives for true-time and long-term health monitoring, simultaneously.

Enabling Batteryless Wearable Devices by Transferring Power Through The Human Body

2021 ◽  
Vol 24 (3) ◽  
pp. 30-34
Author(s):  
Rishi Shukla ◽  
Neev Kiran ◽  
Rui Wang ◽  
Jeremy Gummeson ◽  
Sunghoon Ivan Lee
Keyword(s):  
Energy Source ◽  
Low Power ◽  
Human Body ◽  
Wearable Sensors ◽  
Primary Energy ◽  
Wearable Devices ◽  
Ultra Low Power ◽  
The Past ◽  
Key Barrier

Over the past few decades, we have witnessed tremendous advancements in semiconductor and MEMS technologies, leading to the proliferation of ultra-miniaturized and ultra-low-power (in micro-watt ranges) wearable devices for wellness and healthcare [1]. Most of these wearable sensors are battery powered for their operation. The use of an on-device battery as the primary energy source poses a number of challenges that serve as the key barrier to the development of novel wearable applications and the widespread use of numerous, seamless wearable sensors [5].

The Feasibility and Effectiveness of Wearable Sensor Technology in the Management of Elderly Diabetics with Foot Ulcer Remission: A Proof-Of-Concept Pilot Study with Six Cases

Gerontology ◽  
2021 ◽  
pp. 1-10
Author(s):  
Chenzhen Du ◽  
Hongyan Wang ◽  
Heming Chen ◽  
Xiaoyun Fan ◽  
Dongliang Liu ◽  
...  
Keyword(s):  
Odds Ratio ◽  
Stride Length ◽  
Wearable Sensors ◽  
Foot Ulcer ◽  
Wearable Devices ◽  
Foot Ulcers ◽  
Diabetic Patients ◽  
Wearable Sensor ◽  
Double Support ◽  
Gait Parameters

Aims: Using specials wearable sensors, we explored changes in gait and balance parameters, over time, in elderly patients at high risk of diabetic foot, wearing different types of footwear. This assessed the relationship between gait and balance changes in elderly diabetic patients and the development of foot ulcers, in a bid to uncover potential benefits of wearable devices in the prognosis and management of the aforementioned complication. Methods: A wearable sensor-based monitoring system was used in middle-elderly patients with diabetes who recently recovered from neuropathic plantar foot ulcers. A total of 6 patients (age range: 55–80 years) were divided into 2 groups: the therapeutic footwear group (n = 3) and the regular footwear (n = 3) group. All subjects were assessed for gait and balance throughout the study period. Walking ability and gait pattern were assessed by allowing participants to walk normally for 1 min at habitual speed. The balance assessment program incorporated the “feet together” standing test and the instrumented modified Clinical Test of Sensory Integration and Balance. Biomechanical information was monitored at least 3 times. Results: We found significant differences in stride length (p < 0.0001), stride velocity (p < 0.0001), and double support (p < 0.0001) between the offloading footwear group (OG) and the regular footwear group on a group × time interaction. The balance test embracing eyes-open condition revealed a significant difference in Hip Sway (p = 0.004), COM Range ML (p = 0.008), and COM Position (p = 0.004) between the 2 groups. Longitudinally, the offloading group exhibited slight improvement in the performance of gait parameters over time. The stride length (odds ratio 3.54, 95% CI 1.34–9.34, p = 0.018) and velocity (odds ratio 3.13, 95% CI 1.19–8.19, p = 0.033) of OG patients increased, converse to the double-support period (odds ratio 6.20, 95% CI 1.97–19.55, p = 0.002), which decreased. Conclusions: Special wearable devices can accurately monitor gait and balance parameters in patients in real time. The finding reveals the feasibility and effectiveness of advanced wearable sensors in the prevention and management of diabetic foot ulcer and provides a solid background for future research. In addition, the development of foot ulcers in elderly diabetic patients may be associated with changes in gait parameters and the nature of footwear. Even so, larger follow-up studies are needed to validate our findings.

scholarly journals Recent Advances in Wearable Devices for Non-Invasive Sensing

2021 ◽  
Vol 11 (3) ◽  
pp. 1235
Author(s):  
Su Min Yun ◽  
Moohyun Kim ◽  
Yong Won Kwon ◽  
Hyobeom Kim ◽  
Mi Jung Kim ◽  
...  
Keyword(s):  
Health Monitoring ◽  
Data Transmission ◽  
Wearable Sensors ◽  
Wearable Devices ◽  
Wireless Power ◽  
Non Invasive ◽  
Sensing Systems ◽  
Recent Advances ◽  
Wearable Systems ◽  
Precise Diagnosis

The development of wearable sensors is aimed at enabling continuous real-time health monitoring, which leads to timely and precise diagnosis anytime and anywhere. Unlike conventional wearable sensors that are somewhat bulky, rigid, and planar, research for next-generation wearable sensors has been focused on establishing fully-wearable systems. To attain such excellent wearability while providing accurate and reliable measurements, fabrication strategies should include (1) proper choices of materials and structural designs, (2) constructing efficient wireless power and data transmission systems, and (3) developing highly-integrated sensing systems. Herein, we discuss recent advances in wearable devices for non-invasive sensing, with focuses on materials design, nano/microfabrication, sensors, wireless technologies, and the integration of those.

scholarly journals Diabetes classification application with efficient missing and outliers data handling algorithms

2021 ◽  
Author(s):  
Hanaa Torkey ◽  
Elhossiny Ibrahim ◽  
EZZ El-Din Hemdan ◽  
Ayman El-Sayed ◽  
Marwa A. Shouman
Keyword(s):  
Deep Learning ◽  
Healthcare System ◽  
Wearable Sensors ◽  
Model Performance ◽  
Analysis Model ◽  
Human Errors ◽  
Web Based ◽  
Machine Learning Model ◽  
Complete Dataset ◽  
Smart Wearable

AbstractCommunication between sensors spread everywhere in healthcare systems may cause some missing in the transferred features. Repairing the data problems of sensing devices by artificial intelligence technologies have facilitated the Medical Internet of Things (MIoT) and its emerging applications in Healthcare. MIoT has great potential to affect the patient's life. Data collected from smart wearable devices size dramatically increases with data collected from millions of patients who are suffering from diseases such as diabetes. However, sensors or human errors lead to missing some values of the data. The major challenge of this problem is how to predict this value to maintain the data analysis model performance within a good range. In this paper, a complete healthcare system for diabetics has been used, as well as two new algorithms are developed to handle the crucial problem of missed data from MIoT wearable sensors. The proposed work is based on the integration of Random Forest, mean, class' mean, interquartile range (IQR), and Deep Learning to produce a clean and complete dataset. Which can enhance any machine learning model performance. Moreover, the outliers repair technique is proposed based on dataset class detection, then repair it by Deep Learning (DL). The final model accuracy with the two steps of imputation and outliers repair is 97.41% and 99.71% Area Under Curve (AUC). The used healthcare system is a web-based diabetes classification application using flask to be used in hospitals and healthcare centers for the patient diagnosed with an effective fashion.

Review on Reliable and Quality Wearable Healthcare Device (WHD)

2021 ◽  
Vol 10 (4) ◽  
pp. 1-25
Author(s):  
Nimi W. S. ◽  
P. Subha Hency Jose ◽  
Jegan R.
Keyword(s):  
Health Monitoring ◽  
Wearable Devices ◽  
Health Condition ◽  
Wearable Device ◽  
Smart Device ◽  
Signal Acquisition ◽  
Medical Practitioners ◽  
Quality Signal ◽  
Patient Health ◽  
Smart Wearable

This paper presents a brief review on present developments in wearable devices and their importance in healthcare networks. The state-of-the-art system architecture on wearable healthcare devices and their design techniques are reviewed and becomes an essential step towards developing a smart device for various biomedical applications which includes diseases classifications and detection, analyzing nature of the bio signals, vital parameters measurement, and e-health monitoring through noninvasive method. From the review on latest published research papers on medical wearable device and bio signal analysis, it can be concluded that it is more important and very essential to design and develop a smart wearable device in healthcare environment for quality signal acquisition and e-health monitoring which leads to effective measures of multiparameter extractions. This will help the medical practitioners to understand the nature of patient health condition easily by visualizing a quality signal by smart wearable devices.

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