scholarly journals A Contactless Method for Measuring Full-Day, Naturalistic Motor Behavior Using Wearable Inertial Sensors

2021 ◽  
Vol 12 ◽  
Author(s):  
John M. Franchak ◽  
Vanessa Scott ◽  
Chuan Luo
Keyword(s):  
Machine Learning ◽  
Inertial Sensors ◽  
Motor Behavior ◽  
Body Position ◽  
Video Recording ◽  
Machine Learning Algorithms ◽  
Contactless Method ◽  
Developmental Experience ◽  
Wearable Inertial Sensors ◽  
Full Day

How can researchers best measure infants' motor experiences in the home? Body position—whether infants are held, supine, prone, sitting, or upright—is an important developmental experience. However, the standard way of measuring infant body position, video recording by an experimenter in the home, can only capture short instances, may bias measurements, and conflicts with physical distancing guidelines resulting from the COVID-19 pandemic. Here, we introduce and validate an alternative method that uses machine learning algorithms to classify infants' body position from a set of wearable inertial sensors. A laboratory study of 15 infants demonstrated that the method was sufficiently accurate to measure individual differences in the time that infants spent in each body position. Two case studies showed the feasibility of applying this method to testing infants in the home using a contactless equipment drop-off procedure.

scholarly journals Analysis of Machine Learning-Based Assessment for Elbow Spasticity Using Inertial Sensors

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1622 ◽  
Author(s):  
Jung-Yeon Kim ◽  
Geunsu Park ◽  
Seong-A Lee ◽  
Yunyoung Nam
Keyword(s):  
Machine Learning ◽  
Inertial Sensors ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Lower Limbs ◽  
Support Vector ◽  
Multilayer Perceptrons ◽  
Test Machine ◽  
Linear Discriminant ◽  
Passive Stretch

Spasticity is a frequently observed symptom in patients with neurological impairments. Spastic movements of their upper and lower limbs are periodically measured to evaluate functional outcomes of physical rehabilitation, and they are quantified by clinical outcome measures such as the modified Ashworth scale (MAS). This study proposes a method to determine the severity of elbow spasticity, by analyzing the acceleration and rotation attributes collected from the elbow of the affected side of patients and machine-learning algorithms to classify the degree of spastic movement; this approach is comparable to assigning an MAS score. We collected inertial data from participants using a wearable device incorporating inertial measurement units during a passive stretch test. Machine-learning algorithms—including decision tree, random forests (RFs), support vector machine, linear discriminant analysis, and multilayer perceptrons—were evaluated in combinations of two segmentation techniques and feature sets. A RF performed well, achieving up to 95.4% accuracy. This work not only successfully demonstrates how wearable technology and machine learning can be used to generate a clinically meaningful index but also offers rehabilitation patients an opportunity to monitor the degree of spasticity, even in nonhealthcare institutions where the help of clinical professionals is unavailable.

scholarly journals An Evaluation of Wearable Inertial Sensor Configuration and Supervised Machine Learning Models for Automatic Punch Classification in Boxing

IoT ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 360-381
Author(s):  
Matthew T. O. Worsey ◽  
Hugo G. Espinosa ◽  
Jonathan B. Shepherd ◽  
David V. Thiel
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Supervised Machine Learning ◽  
Learning Models ◽  
Significant Statistical Difference ◽  
Sensor Configuration ◽  
Wearable Inertial Sensors ◽  
Machine Learning Models

Machine learning is a powerful tool for data classification and has been used to classify movement data recorded by wearable inertial sensors in general living and sports. Inertial sensors can provide valuable biofeedback in combat sports such as boxing; however, the use of such technology has not had a global uptake. If simple inertial sensor configurations can be used to automatically classify strike type, then cumbersome tasks such as video labelling can be bypassed and the foundation for automated workload monitoring of combat sport athletes is set. This investigation evaluates the classification performance of six different supervised machine learning models (tuned and untuned) when using two simple inertial sensor configurations (configuration 1—inertial sensor worn on both wrists; configuration 2—inertial sensor worn on both wrists and third thoracic vertebrae [T3]). When trained on one athlete, strike prediction accuracy was good using both configurations (sensor configuration 1 mean overall accuracy: 0.90 ± 0.12; sensor configuration 2 mean overall accuracy: 0.87 ± 0.09). There was no significant statistical difference in prediction accuracy between both configurations and tuned and untuned models (p > 0.05). Moreover, there was no significant statistical difference in computational training time for tuned and untuned models (p > 0.05). For sensor configuration 1, a support vector machine (SVM) model with a Gaussian rbf kernel performed the best (accuracy = 0.96), for sensor configuration 2, a multi-layered perceptron neural network (MLP-NN) model performed the best (accuracy = 0.98). Wearable inertial sensors can be used to accurately classify strike-type in boxing pad work, this means that cumbersome tasks such as video and notational analysis can be bypassed. Additionally, automated workload and performance monitoring of athletes throughout training camp is possible. Future investigations will evaluate the performance of this algorithm on a greater sample size and test the influence of impact window-size on prediction accuracy. Additionally, supervised machine learning models should be trained on data collected during sparring to see if high accuracy holds in a competition setting. This can help move closer towards automatic scoring in boxing.

scholarly journals Systematic review on the application of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Fabian Marcel Rast ◽  
Rob Labruyère
Keyword(s):  
Systematic Review ◽  
Motor Activity ◽  
Everyday Life ◽  
Outcome Measures ◽  
Inertial Sensors ◽  
Body Position ◽  
Relevant Literature ◽  
Measurement Properties ◽  
Mobility Impairments ◽  
Wearable Inertial Sensors

Abstract Background Recent advances in wearable sensor technologies enable objective and long-term monitoring of motor activities in a patient’s habitual environment. People with mobility impairments require appropriate data processing algorithms that deal with their altered movement patterns and determine clinically meaningful outcome measures. Over the years, a large variety of algorithms have been published and this review provides an overview of their outcome measures, the concepts of the algorithms, the type and placement of required sensors as well as the investigated patient populations and measurement properties. Methods A systematic search was conducted in MEDLINE, EMBASE, and SCOPUS in October 2019. The search strategy was designed to identify studies that (1) involved people with mobility impairments, (2) used wearable inertial sensors, (3) provided a description of the underlying algorithm, and (4) quantified an aspect of everyday life motor activity. The two review authors independently screened the search hits for eligibility and conducted the data extraction for the narrative review. Results Ninety-five studies were included in this review. They covered a large variety of outcome measures and algorithms which can be grouped into four categories: (1) maintaining and changing a body position, (2) walking and moving, (3) moving around using a wheelchair, and (4) activities that involve the upper extremity. The validity or reproducibility of these outcomes measures was investigated in fourteen different patient populations. Most of the studies evaluated the algorithm’s accuracy to detect certain activities in unlabeled raw data. The type and placement of required sensor technologies depends on the activity and outcome measure and are thoroughly described in this review. The usability of the applied sensor setups was rarely reported. Conclusion This systematic review provides a comprehensive overview of applications of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments. It summarizes the state-of-the-art, it provides quick access to the relevant literature, and it enables the identification of gaps for the evaluation of existing and the development of new algorithms.

scholarly journals Automatic Detection of Faults in Race Walking: A Comparative Analysis of Machine-Learning Algorithms Fed with Inertial Sensor Data

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1461 ◽  
Author(s):  
Juri Taborri ◽  
Eduardo Palermo ◽  
Stefano Rossi
Keyword(s):  
Machine Learning ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Learning Algorithms ◽  
Automatic Detection ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Support Vector ◽  
Angular Velocities ◽  
Race Walking

The validity of results in race walking is often questioned due to subjective decisions in the detection of faults. This study aims to compare machine-learning algorithms fed with data gathered from inertial sensors placed on lower-limb segments to define the best-performing classifiers for the automatic detection of illegal steps. Eight race walkers were enrolled and linear accelerations and angular velocities related to pelvis, thighs, shanks, and feet were acquired by seven inertial sensors. The experimental protocol consisted of two repetitions of three laps of 250 m, one performed with regular race walking, one with loss-of-contact faults, and one with knee-bent faults. The performance of 108 classifiers was evaluated in terms of accuracy, recall, precision, F1-score, and goodness index. Generally, linear accelerations revealed themselves as more characteristic with respect to the angular velocities. Among classifiers, those based on the support vector machine (SVM) were the most accurate. In particular, the quadratic SVM fed with shank linear accelerations was the best-performing classifier, with an F1-score and a goodness index equal to 0.89 and 0.11, respectively. The results open the possibility of using a wearable device for automatic detection of faults in race walking competition.

scholarly journals Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning

2021 ◽  
Vol 11 (11) ◽  
pp. 4945
Author(s):  
Axel Sepúlveda ◽  
Francisco Castillo ◽  
Carlos Palma ◽  
Maria Rodriguez-Fernandez
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Video Recording ◽  
Recognition Task ◽  
Machine Learning Algorithms ◽  
Physiological Signals ◽  
Emotional States ◽  
Ecg Signals ◽  
Dimensional Classification ◽  
Wide Range

Affect detection combined with a system that dynamically responds to a person’s emotional state allows an improved user experience with computers, systems, and environments and has a wide range of applications, including entertainment and health care. Previous studies on this topic have used a variety of machine learning algorithms and inputs such as audial, visual, or physiological signals. Recently, a lot of interest has been focused on the last, as speech or video recording is impractical for some applications. Therefore, there is a need to create Human–Computer Interface Systems capable of recognizing emotional states from noninvasive and nonintrusive physiological signals. Typically, the recognition task is carried out from electroencephalogram (EEG) signals, obtaining good accuracy. However, EEGs are difficult to register without interfering with daily activities, and recent studies have shown that it is possible to use electrocardiogram (ECG) signals for this purpose. This work improves the performance of emotion recognition from ECG signals using wavelet transform for signal analysis. Features of the ECG signal are extracted from the AMIGOS database using a wavelet scattering algorithm that allows obtaining features of the signal at different time scales, which are then used as inputs for different classifiers to evaluate their performance. The results show that the proposed algorithm for extracting features and classifying the signals obtains an accuracy of 88.8% in the valence dimension, 90.2% in arousal, and 95.3% in a two-dimensional classification, which is better than the performance reported in previous studies. This algorithm is expected to be useful for classifying emotions using wearable devices.

scholarly journals Intelligent System for the Prevention of Pressure Ulcers by Monitoring Postural Changes with Wearable Inertial Sensors

Proceedings ◽  
2019 ◽  
Vol 31 (1) ◽  
pp. 79 ◽  
Author(s):  
Edna Bernal Monroy ◽  
Daniel Zafra Romero ◽  
Macarena Espinilla Estévez ◽  
Federico Cruciani ◽  
Ian Cleland ◽  
...  
Keyword(s):  
Pressure Ulcers ◽  
Inertial Sensors ◽  
Intelligent System ◽  
Body Position ◽  
Experimental Tests ◽  
Historical Record ◽  
Position Estimation ◽  
Learning Approaches ◽  
Postural Changes ◽  
Wearable Inertial Sensors

Pressure ulcers affect the quality of life and health level of patients while causing a cost increase for the health system. Despite all of this, there is no monitoring system for this disease, even though it is very common in hospitalized patients. To resolve this issue, in this work, we present a prototype for monitoring and caring of pressure ulcers, through an intelligent system based on wearable inertial devices. The system detects the position of patients with reduced mobility while in bed, walking, or standing; providing, to medical staff and caregivers, useful information by means of mobile devices, in particular: (i) A historical record of body activity and (ii) notifications when a single position persists for a prolonged period of time. The following contributions are presented in this paper. Firstly, a technological survey of healthcare systems applied to pressure ulcers is presented. Secondly, an architecture and a methodology for modelling wearable inertial sensors for classifying body position are proposed. Finally, experimental tests with real cases were conducted to evaluate machine learning approaches and to validate the position estimation of the case study developed with four participants.

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