Wearable Triboelectric Sensors Enabled Gait Analysis and Waist Motion Capture for IoT‐Based Smart Healthcare Applications

For Motion Capture in Gait Analysis using Known Spherical Markers one simple direct approach is to compute the projection of the Marker Center using its projection in the Pixel Plane and based on it to find the location of the Marker on the line that connects the Marker Center Projection and the camera Focal Point. For various positions of the Marker in the workspace the exact image of the marker is computed using a genuine approach and compute back the approximation of the position based on the generated image. Various algorithms are taken in consideration and finally the results are assessed from the point of view of Gait Analysis and two directions for calculus improvement are identified.

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Validation of wearable inertial sensor-based gait analysis system for measurement of spatiotemporal parameters and lower extremity joint kinematics in sagittal plane

Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine ◽

10.1177/09544119211072971 ◽

2022 ◽

pp. 095441192110729

Author(s):

Gunjan Patel ◽

Rajani Mullerpatan ◽

Bela Agarwal ◽

Triveni Shetty ◽

Rajdeep Ojha ◽

...

Keyword(s):

Gait Analysis ◽

Motion Analysis ◽

Motion Capture ◽

Inertial Sensor ◽

Joint Kinematics ◽

Percentage Error ◽

Motion Capture System ◽

Spatiotemporal Parameters ◽

Analysis System ◽

Motion Capture Systems

Wearable inertial sensor-based motion analysis systems are promising alternatives to standard camera-based motion capture systems for the measurement of gait parameters and joint kinematics. These wearable sensors, unlike camera-based gold standard systems, find usefulness in outdoor natural environment along with confined indoor laboratory-based environment due to miniature size and wireless data transmission. This study reports validation of our developed (i-Sens) wearable motion analysis system against standard motion capture system. Gait analysis was performed at self-selected speed on non-disabled volunteers in indoor ( n = 15) and outdoor ( n = 8) environments. Two i-Sens units were placed at the level of knee and hip along with passive markers (for indoor study only) for simultaneous 3D motion capture using a motion capture system. Mean absolute percentage error (MAPE) was computed for spatiotemporal parameters from the i-Sens system versus the motion capture system as a true reference. Mean and standard deviation of kinematic data for a gait cycle were plotted for both systems against normative data. Joint kinematics data were analyzed to compute the root mean squared error (RMSE) and Pearson’s correlation coefficient. Kinematic plots indicate a high degree of accuracy of the i-Sens system with the reference system. Excellent positive correlation was observed between the two systems in terms of hip and knee joint angles (Indoor: hip 3.98° ± 1.03°, knee 6.48° ± 1.91°, Outdoor: hip 3.94° ± 0.78°, knee 5.82° ± 0.99°) with low RMSE. Reliability characteristics (defined using standard statistical thresholds of MAPE) of stride length, cadence, walking speed in both outdoor and indoor environment were well within the “Good” category. The i-Sens system has emerged as a potentially cost-effective, valid, accurate, and reliable alternative to expensive, standard motion capture systems for gait analysis. Further clinical trials using the i-Sens system are warranted on participants across different age groups.

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Cloud based Secure Watermarking using IWT-Schur-RSVD with Fuzzy Inference System for Smart Healthcare Applications

Sustainable Cities and Society ◽

10.1016/j.scs.2021.103398 ◽

2021 ◽

pp. 103398

Author(s):

Ashima Anand ◽

Amit Kumar Singh

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Healthcare Applications ◽

Inference System ◽

Smart Healthcare

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Human Gait-labeling Uncertainty and a Hybrid Model for Gait Segmentation

10.36227/techrxiv.16924102.v1 ◽

2021 ◽

Author(s):

Jiaen Wu ◽

Henrik Maurenbrecher ◽

Alessandro Schaer ◽

Barna Becsek ◽

Chris Awai Easthope ◽

...

Keyword(s):

Gait Analysis ◽

Motion Capture ◽

Ground Truth ◽

Absolute Error ◽

Human Gait ◽

Heel Strike ◽

Analysis Model ◽

Flat Foot ◽

Dynamic Time ◽

Human Gait Analysis

<div><div><div><p>Motion capture systems are widely accepted as ground-truth for gait analysis and are used for the validation of other gait analysis systems.To date, their reliability and limitations in manual labeling of gait events have not been studied.</p><p><b>Objectives</b>: Evaluate human manual labeling uncertainty and introduce a new hybrid gait analysis model for long-term monitoring.</p><p><b>Methods</b>: Evaluate and estimate inter-labeler inconsistencies by computing the limits-of-agreement; develop a model based on dynamic time warping and convolutional neural network to identify a valid stride and eliminate non-stride data in walking inertial data collected by a wearable device; Gait events are detected within a valid stride region afterwards; This method makes the subsequent data computation more efficient and robust.</p><p><b>Results</b>: The limits of inter-labeler agreement for key</p><p>gait events of heel off, toe off, heel strike, and flat foot are 72 ms, 16 ms, 22 ms, and 80 ms, respectively; The hybrid model's classification accuracy for a stride and a non-stride are 95.16% and 84.48%, respectively; The mean absolute error for detected heel off, toe off, heel strike, and flat foot are 24 ms, 5 ms, 9 ms, and 13 ms, respectively.</p><p><b>Conclusions</b>: The results show the inherent label uncertainty and the limits of human gait labeling of motion capture data; The proposed hybrid-model's performance is comparable to that of human labelers and it is a valid model to reliably detect strides in human gait data.</p><p><b>Significance</b>: This work establishes the foundation for fully automated human gait analysis systems with performances comparable to human-labelers.</p></div></div></div>

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Rehabilitation Assistance Using Motion Capture Devices and Virtual Reality Feedback

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.888.37 ◽

2019 ◽

Vol 888 ◽

pp. 37-42 ◽

Cited By ~ 3

Author(s):

Yasushi Yuminaka ◽

Motoaki Fujii ◽

Atsuhi Manabe ◽

Makoto Hasegawa ◽

Naoki Wada

Keyword(s):

Virtual Reality ◽

Motion Capture ◽

Functional Therapy ◽

Healthcare Applications ◽

Timed Up And Go ◽

Medical Needs ◽

Muscular Disorders ◽

Walking Rehabilitation ◽

Effective Assessment ◽

Medical And Healthcare

Physical rehabilitation is required to support functional therapy in patients with restricted function in their body caused by cerebral, spinal, or muscular disorders. We sought to investigate the feasibility of medical and healthcare applications of the Kinect v2 motion capture devices and a head mount display in response to practical medical needs, including: (1) a Timed Up and Go test, and a walking rehabilitation support system; and (2) rehabilitation assistance using virtual reality feedback. The prototype systems demonstrate that the ICT-based rehabilitation equipment offers the objective and effective assessment of physical deficits in patients with conditions such as stroke or Parkinson's disease.

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