Application of Deep Learning to IMU sensor motion

Christian, Matthew; Uyanik, Cihan; Erdemir, Erdem; Kaplanoğlu, Erkan; Bhattacharya, Sambit; Bailey, Rashad; Kawamura, K.; Hargrove, S. Keith

doi:10.1109/southeastcon42311.2019.9020363

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…As is seen in previous studies, the deep learning model used alongside the IMU varies. Requiring a time series based solution, recurrent neural networks, particularly LSTM, and convolutional neural network (CNN) models have been implemented with significant success Rivera et al (2017), Christian et al (2019). Combinations of CNN layers with LSTM models have also been effective in processing IMU data Silva do Monte Lima et al (2019).…”

Section: Understanding Hand Movementmentioning

confidence: 99%

FaceGuard: A Wearable System To Avoid Face Touching

et al. 2021

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Most people touch their faces unconsciously, for instance to scratch an itch or to rest one’s chin in their hands. To reduce the spread of the novel coronavirus (COVID-19), public health officials recommend against touching one’s face, as the virus is transmitted through mucous membranes in the mouth, nose and eyes. Students, office workers, medical personnel and people on trains were found to touch their faces between 9 and 23 times per hour. This paper introduces FaceGuard, a system that utilizes deep learning to predict hand movements that result in touching the face, and provides sensory feedback to stop the user from touching the face. The system utilizes an inertial measurement unit (IMU) to obtain features that characterize hand movement involving face touching. Time-series data can be efficiently classified using 1D-Convolutional Neural Network (CNN) with minimal feature engineering; 1D-CNN filters automatically extract temporal features in IMU data. Thus, a 1D-CNN based prediction model is developed and trained with data from 4,800 trials recorded from 40 participants. Training data are collected for hand movements involving face touching during various everyday activities such as sitting, standing, or walking. Results showed that while the average time needed to touch the face is 1,200 ms, a prediction accuracy of more than 92% is achieved with less than 550 ms of IMU data. As for the sensory response, the paper presents a psychophysical experiment to compare the response time for three sensory feedback modalities, namely visual, auditory, and vibrotactile. Results demonstrate that the response time is significantly smaller for vibrotactile feedback (427.3 ms) compared to visual (561.70 ms) and auditory (520.97 ms). Furthermore, the success rate (to avoid face touching) is also statistically higher for vibrotactile and auditory feedback compared to visual feedback. These results demonstrate the feasibility of predicting a hand movement and providing timely sensory feedback within less than a second in order to avoid face touching.

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Section: Understanding Hand Movementmentioning

confidence: 99%

FaceGuard: A Wearable System To Avoid Face Touching

et al. 2021

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“…However, the performance of these approaches depends on sufficient quantity and diversity of training data, which may not always be practical to obtain. An inadequate training dataset may result in limited generalizability and model robustness to new subjects and different onboard MEMS hardware configurations, and an inability to predict new pathological movements [ 134 , 135 , 136 , 137 ]. Furthermore, the generation of artificial neural networks such as GANs can be challenging due to the large number of hyperparameters that require tuning, which has limited their uptake to date.…”

Section: Discussionmentioning

confidence: 99%

Conversion of Upper-Limb Inertial Measurement Unit Data to Joint Angles: A Systematic Review

Fang

Woodford

Senanayake

et al. 2023

Sensors

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Inertial measurement units (IMUs) have become the mainstay in motion evaluation outside of the laboratory; however, quantification of 3-dimensional upper limb motion using IMUs remains challenging. The objective of this systematic review is twofold. Firstly, to evaluate computational methods used to convert IMU data to joint angles in the upper limb, including for the scapulothoracic, humerothoracic, glenohumeral, and elbow joints; the second was to quantify the accuracy of these approaches when compared to optoelectronic motion analysis. Fifty-two studies were included. Maximum joint motion measurement accuracy from IMUs was achieved using Euler angle decomposition and Kalman-based filters. This resulted in differences between IMU and optoelectronic motion analysis of 4° across all degrees of freedom of humerothoracic movement. Higher accuracy has been achieved at the elbow joint with functional joint axis calibration tasks and the use of kinematic constraints on gyroscope data, resulting in RMS errors between IMU and optoelectronic motion for flexion–extension as low as 2°. For the glenohumeral joint, 3D joint motion has been described with RMS errors of 6° and higher. In contrast, scapulothoracic joint motion tracking yielded RMS errors in excess of 10° in the protraction–retraction and anterior-posterior tilt direction. The findings of this study demonstrate high-quality 3D humerothoracic and elbow joint motion measurement capability using IMUs and underscore the challenges of skin motion artifacts in scapulothoracic and glenohumeral motion analysis. Future studies ought to implement functional joint axis calibrations, and IMU-based scapular locators to address skin motion artifacts at the scapula, and explore the use of artificial neural networks and data-driven approaches to directly convert IMU data to joint angles.

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A new in-air handwritten persian characters recognition method based on inertial sensor position estimation and convolutional neural network

Meshkat

Abdali-Mohammadi

2022

J Ambient Intell Human Comput

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Application of Deep Learning to IMU sensor motion

Cited by 8 publications

References 5 publications

FaceGuard: A Wearable System To Avoid Face Touching

FaceGuard: A Wearable System To Avoid Face Touching

Conversion of Upper-Limb Inertial Measurement Unit Data to Joint Angles: A Systematic Review

A new in-air handwritten persian characters recognition method based on inertial sensor position estimation and convolutional neural network

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