scholarly journals Privacy-preserving IoT Framework for Activity Recognition in Personal Healthcare Monitoring

2020 ◽  
Vol 2 (1) ◽  
pp. 1-22
Author(s):  
Theo Jourdan ◽  
Antoine Boutet ◽  
Amine Bahi ◽  
Carole Frindel
Keyword(s):  
Activity Recognition ◽  
Privacy Preserving ◽  
Personal Healthcare ◽  
Healthcare Monitoring

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.

A stretchable fiber nanogenerator for versatile mechanical energy harvesting and self-powered full-range personal healthcare monitoring

Nano Energy ◽  
2017 ◽  
Vol 41 ◽  
pp. 511-518 ◽  
Author(s):  
Yin Cheng ◽  
Xin Lu ◽  
Kwok Hoe Chan ◽  
Ranran Wang ◽  
Zherui Cao ◽  
...  
Keyword(s):  
Energy Harvesting ◽  
Mechanical Energy ◽  
Full Range ◽  
Personal Healthcare ◽  
Healthcare Monitoring ◽  
Self Powered

scholarly journals An Activity Recognition Framework for Monitoring Non-Steady-State Locomotion of Individuals with Parkinson’s Disease

2021 ◽  
Author(s):  
Mahdieh Kazemimoghadam ◽  
Nicholas Fey
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Steady State ◽  
Activity Recognition ◽  
Short Term Memory ◽  
Upper Body ◽  
Linear Discriminant ◽  
Healthcare Monitoring ◽  
Task Recognition ◽  
Improved Performance

Abstract Background: Fundamental knowledge in activity recognition of individuals with motor disorders such as Parkinson’s disease (PD) has been primarily limited to detection of steady-state/static tasks (e.g., sitting, standing, walking), and identification of non-steady-state locomotion on uneven terrains (stairs, ramps) has not received much attention. Furthermore, previous research has mainly relied on data from a large number of locations which could adversely affect user convenience and system performance. Methods: Here, individuals with mild stages of PD and healthy subjects performed non-steady-state circuit trials comprising stairs, ramp, and changes of direction. An offline analysis using a linear discriminant analysis (LDA) classifier and a Long-Short Term Memory (LSTM) neural network was performed for task recognition. The performance of accelerographic and gyroscopic information from varied lower/upper-body segments were tested across a set of user-independent and user-dependent training paradigms. Results: Comparing the F1 score of a given segment across classifiers showed improved performance using LSTM compared to LDA. Using LSTM, even a subset of information (e.g., feet data) in subject-independent paradigms appeared to provide F1 score > 0.8. However, employing LDA was shown to be at the expense of being limited to using a subject-dependent paradigm and/or biomechanical data from multiple body locations. Conclusion: The findings could inform a number of applications in the field of healthcare monitoring and developing advanced lower-limb assistive devices by providing insights into classification schemes capable of handling non-steady-state and unstructured locomotion in individuals with mild Parkinson’s disease.

Design and Development of Wearable Human Activity Recognition for Healthcare Monitoring

2020 ◽  
pp. 323-332
Author(s):  
Hamzah Ahmad ◽  
Nurul Syafiqah Mohd ◽  
Nur Aqilah Othman ◽  
Mohd Mawardi Saari ◽  
Mohd Syakirin Ramli
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Design And Development ◽  
Healthcare Monitoring

scholarly journals Machine Learning Algorithms for Activity-Intensity Recognition Using Accelerometer Data

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1214
Author(s):  
Eduardo Gomes ◽  
Luciano Bertini ◽  
Wagner Rangel Campos ◽  
Ana Paula Sobral ◽  
Izabela Mocaiber ◽  
...  
Keyword(s):  
Random Forest ◽  
Activity Recognition ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Relevant Parameter ◽  
Accelerometer Data ◽  
K Nearest Neighbors ◽  
Activity Intensity ◽  
Potential Applications ◽  
Healthcare Monitoring

In pervasive healthcare monitoring, activity recognition is critical information for adequate management of the patient. Despite the great number of studies on this topic, a contextually relevant parameter that has received less attention is intensity recognition. In the present study, we investigated the potential advantage of coupling activity and intensity, namely, Activity-Intensity, in accelerometer data to improve the description of daily activities of individuals. We further tested two alternatives for supervised classification. In the first alternative, the activity and intensity are inferred together by applying a single classifier algorithm. In the other alternative, the activity and intensity are classified separately. In both cases, the algorithms used for classification are k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). The results showed the viability of the classification with good accuracy for Activity-Intensity recognition. The best approach was KNN implemented in the single classifier alternative, which resulted in 79% of accuracy. Using two classifiers, the result was 97% accuracy for activity recognition (Random Forest), and 80% for intensity recognition (KNN), which resulted in 78% for activity-intensity coupled. These findings have potential applications to improve the contextualized evaluation of movement by health professionals in the form of a decision system with expert rules.

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