scholarly journals A Subject-Specific Approach to Detect Fatigue-Related Changes in Spine Motion Using Wearable Sensors

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2646 ◽  
Author(s):  
Victor C.H. Chan ◽  
Shawn M. Beaudette ◽  
Kenneth B. Smale ◽  
Kristen H.E. Beange ◽  
Ryan B. Graham
Keyword(s):  
Muscle Fatigue ◽  
Wearable Sensors ◽  
Composite Index ◽  
Spine Kinematics ◽  
Flexion Extension ◽  
Main Effect ◽  
Subject Specific ◽  
Spine Motion ◽  
Measurement Units ◽  
The Individual

An objective method to detect muscle fatigue-related kinematic changes may reduce workplace injuries. However, heterogeneous responses to muscle fatigue suggest that subject-specific analyses are necessary. The objectives of this study were to: (1) determine if wearable inertial measurement units (IMUs) could be used in conjunction with a spine motion composite index (SMCI) to quantify subject-specific changes in spine kinematics during a repetitive spine flexion-extension (FE) task; and (2) determine if the SMCI was correlated with measures of global trunk muscle fatigue. Spine kinematics were measured using wearable IMUs in 10 healthy adults during a baseline set followed by 10 sets of 50 spine FE repetitions. After each set, two fatigue measures were collected: perceived level of fatigue using a visual analogue scale (VAS), and maximal lift strength. SMCIs incorporating 10 kinematic variables from 2 IMUs (pelvis and T8 vertebrae) were calculated and used to quantify subject-specific changes in movement. A main effect of set was observed (F (1.7, 15.32) = 10.42, p = 0.002), where the SMCI became significantly greater than set 1 starting at set 4. Significant correlations were observed between the SMCI and both fatigue VAS and maximal lift strength at the individual and study level. These findings support the use of wearable IMUs to detect subject-specific changes in spine motion associated with muscle fatigue.

scholarly journals Towards Detecting Biceps Muscle Fatigue in Gym Activity Using Wearables

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 759
Author(s):  
Mohamed Elshafei ◽  
Emad Shihab
Keyword(s):  
Muscle Fatigue ◽  
Random Forests ◽  
Linear Models ◽  
Wearable Sensors ◽  
Feedforward Neural Networks ◽  
Biceps Muscle ◽  
Real World Application ◽  
Naturally Occurring ◽  
Subject Specific ◽  
Machine Learning Models

Fatigue is a naturally occurring phenomenon during human activities, but it poses a bigger risk for injuries during physically demanding activities, such as gym activities and athletics. Several studies show that bicep muscle fatigue can lead to various injuries that may require up to 22 weeks of treatment. In this work, we adopt a wearable approach to detect biceps muscle fatigue during a bicep concentration curl exercise as an example of a gym activity. Our dataset consists of 3000 bicep curls from twenty middle-aged volunteers at ages between 27 to 30 and Body Mass Index (BMI) ranging between 18 to 28. All volunteers have been gym-goers for at least 1 year with no records of chronic diseases, muscle, or bone surgeries. We encountered two main challenges while collecting our dataset. The first challenge was the dumbbell’s suitability, where we found that a dumbbell weight (4.5 kg) provides the best tradeoff between longer recording sessions and the occurrence of fatigue on exercises. The second challenge is the subjectivity of RPE, where we average the reported RPE with the measured heart rate converted to RPE. We observed from our data that fatigue reduces the biceps’ angular velocity; therefore, it increases the completion time for later sets. We extracted a total of 33 features from our dataset, which have been reduced to 16 features. These features are the most overall representative and correlated with bicep curl movement, yet they are fatigue-specific features. We utilized these features in five machine learning models, which are Generalized Linear Models (GLM), Logistic Regression (LR), Random Forests (RF), Decision Trees (DT), and Feedforward Neural Networks (FNN). We found that using a two-layer FNN achieves an accuracy of 98% and 88% for subject-specific and cross-subject models, respectively. The results presented in this work are useful and represent a solid start for moving into a real-world application for detecting the fatigue level in bicep muscles using wearable sensors as we advise athletes to take fatigue into consideration to avoid fatigue-induced injuries.

scholarly journals Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 194
Author(s):  
Sarah Gonzalez ◽  
Paul Stegall ◽  
Harvey Edwards ◽  
Leia Stirling ◽  
Ho Chit Siu
Keyword(s):  
Activity Recognition ◽  
Principal Components ◽  
Classification Accuracy ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Measurement Units ◽  
The Difference

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

scholarly journals Reliability of activity monitors for physical activity assessment in patients with musculoskeletal disorders: A systematic review

2021 ◽  
pp. 1-9
Author(s):  
Francesco Negrini ◽  
Alessandro de Sire ◽  
Stefano Giuseppe Lazzarini ◽  
Federico Pennestrì ◽  
Salvatore Sorce ◽  
...  
Keyword(s):  
Physical Activity ◽  
Systematic Review ◽  
Musculoskeletal Disorders ◽  
Wearable Sensors ◽  
Activity Monitors ◽  
Activity Assessment ◽  
Measurement Tools ◽  
Different Types ◽  
Measurement Units ◽  
Full Text Screening

BACKGROUND: Activity monitors have been introduced in the last years to objectively measure physical activity to help physicians in the management of musculoskeletal patients. OBJECTIVE: This systematic review aimed at describing the assessment of physical activity by commercially available portable activity monitors in patients with musculoskeletal disorders. METHODS: PubMed, Embase, PEDro, Web of Science, Scopus and CENTRAL databases were systematically searched from inception to June 11th, 2020. We considered as eligible observational studies with: musculoskeletal patients; physical activity measured by wearable sensors based on inertial measurement units; comparisons performed with other tools; outcomes consisting of number of steps/day, activity/inactivity time, or activity counts/day. RESULTS: Out of 595 records, after removing duplicates, title/abstract and full text screening, 10 articles were included. We noticed a wide heterogeneity in the wearable devices, that resulted to be 10 different types. Patients included suffered from rheumatoid arthritis, osteoarthritis, juvenile idiopathic arthritis, polymyalgia rheumatica, and fibromyalgia. Only 3 studies compared portable activity trackers with objective measurement tools. CONCLUSIONS: Taken together, this systematic review showed that activity monitors might be considered as useful to assess physical activity in patients with musculoskeletal disorders, albeit, to date, the high device heterogeneity and the different algorithms still prevent their standardization.

Difficulties in the Use of Everyday Technology among Older Adults with Subjective Memory Complaint and Cognitive Decline

Gerontology ◽  
2021 ◽  
pp. 1-9
Author(s):  
Yuriko Ikeda ◽  
Michio Maruta ◽  
Suguru Shimokihara ◽  
Atsushi Nakamura ◽  
Gwanghee Han ◽  
...  
Keyword(s):  
Older Adults ◽  
Cognitive Decline ◽  
Community Dwelling ◽  
Subjective Memory Complaints ◽  
Subjective Memory ◽  
Memory Complaint ◽  
Social Lives ◽  
Early Signs ◽  
Main Effect ◽  
The Individual

<b><i>Introduction:</i></b> The ability to use everyday technology (ET) is becoming increasingly necessary for maintaining domestic and social lives. For older adults, difficulties with using ETs can begin at the mild cognitive impairment (MCI) state and may indicate increasing cognitive decline. The aim of this study was to conduct a detailed investigation into the ability to use ETs among Japanese older community-dwelling adults at 3 stages of cognitive function and the ability to carry out daily activities. <b><i>Method:</i></b> We analyzed family members’ responses to questions about older adults with cognitive decline in their families. A total of 168 older adults with subjective memory complaints (SMC) or cognitive decline and inconvenience in daily life were analyzed. A questionnaire was used to assess the characteristics, ability to use ETs, and ability to manage refrigerator contents, all of which can be early signs of dementia. Participants were divided 3 groups by the type of dementia: SMC (<i>n</i> = 77), MCI (<i>n</i> = 36), and Alzheimer’s disease (AD) (<i>n</i> = 55) for comparison. <b><i>Result:</i></b> The observation list of early signs of dementia (OLD) total score indicated a significant positive correlation with the number of ET errors (<i>r</i> = 0.37, <i>p</i> &#x3c; 0.001) and number of difficulties with refrigerator management (<i>r</i> = 0.18, <i>p</i> = 0.031). Regarding number of ET errors, there was a significant main effect for the 3 groups, and the SMC group made significantly fewer errors than the AD group (<i>p</i> = 0.02). In 7 of the 11 ET categories, errors with using ETs were associated with all 3 groups, with the SMC group making fewer errors, and the AD group making more. Regarding difficulties with refrigerator management, 2 out of 9 problems were associated with the 3 groups, with the SMC group having fewer difficulties and the AD group having more. <b><i>Discussion/Conclusion:</i></b> The results indicated that the ability to use ETs and to manage refrigerator contents begins to decline at the SMC stage. Further evaluation of the ability to use ETs is needed for older adults with SMC so that adequate support in the context of the individual can be provided.

Navicular Drop Before and After Fatigue of the Ankle Invertor Muscles

2013 ◽  
Vol 18 (6) ◽  
pp. 36-39 ◽  
Author(s):  
Fredrick Anthony Gardin ◽  
David Middlemas ◽  
Jennifer L. Williams ◽  
Steven Leigh ◽  
Rob R. Horn
Keyword(s):  
Muscle Fatigue ◽  
Correlation Coefficients ◽  
Standing Position ◽  
Medial Longitudinal Arch ◽  
Intra Class Correlation ◽  
College Age ◽  
Navicular Drop ◽  
Main Effect ◽  
Before And After ◽  
Good Internal Consistency

Context:Navicular drop is widely believed to be an indicator of elevated susceptibility to pronation-related injuries, which may be increased by fatigue in the muscles that dynamically support the medial longitudinal arch.Objective:The purpose of this study was to evaluate navicular drop before and after fatigue of the ankle invertor muscles among individuals with different foot types.Participants:20 male and 16 female recreationally active, college-age volunteers (20.03 ± 1.48 years of age).Methods:Navicular drop was measured before and after inducing fatigue in the ankle invertor muscles. Participants’ foot types were classified as high-arch, neutral, or low-arch.Results:There was no interaction between foot type and trial, and no main effect for trial. A main effect for foot type was significant (p = .001). Intra-class correlation coefficients for prefatigue and postfatigue measurements indicated good internal consistency.Conclusion:Our fndings failed to provide any evidence to support the existence of a relationship between ankle invertor muscle fatigue and static measurements of change in navicular height from a sitting to standing position.

Using in Vivo Subject-Specific Musculotendon Parameters to Investigate Voluntary Movement Changes after Stroke

2014 ◽  
pp. 161-180
Author(s):  
Hujing Hu ◽  
Le Li
Keyword(s):  
Voluntary Movement ◽  
Imaging Techniques ◽  
Movement Trajectory ◽  
Fitting Procedure ◽  
Emg Signal ◽  
Subject Specific ◽  
Elbow Movement ◽  
The Individual ◽  
Neuromusculoskeletal Modeling

Neuromusculoskeletal modeling provides insights into the muscular system which are not always obtained through experiment or observation alone. One of the major challenges in neuromusculoskeletal modeling is to accurately estimate the musculotendon parameters on a subject-specific basis. The latest medical imaging techniques such as ultrasound for the estimation of musculotendon parameters would provide an alternative method to obtain the muscle architecture parameters noninvasively. In this chapter, the feasibility of using ultrasonography to measure the musculotendon parameters of elbow muscles is validated. These parameters help to build a subject-specific EMG-driven model, which could predict the individual muscle force and elbow voluntary movement trajectory using the input of EMG signal without any trajectory fitting procedure involved. The results demonstrate the feasibility of using EMG-driven neuromusculoskeletal modeling with ultrasound-measured data for prediction of voluntary elbow movement for both unimpaired subjects and persons after stroke.

scholarly journals Gait Phase Recognition Using Deep Convolutional Neural Network with Inertial Measurement Units

Biosensors ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 109
Author(s):  
Binbin Su ◽  
Christian Smith ◽  
Elena Gutierrez Farewik
Keyword(s):  
Wearable Sensors ◽  
Sensor Data ◽  
Measurement Unit ◽  
Deep Convolutional Neural Networks ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Gait Phase ◽  
Robotic Devices ◽  
Phase Recognition ◽  
Five Phases

Gait phase recognition is of great importance in the development of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, the user’s current gait phase must first be identified accurately. Gait phase recognition can potentially be achieved through input from wearable sensors. Deep convolutional neural networks (DCNN) is a machine learning approach that is widely used in image recognition. User kinematics, measured from inertial measurement unit (IMU) output, can be considered as an ‘image’ since it exhibits some local ‘spatial’ pattern when the sensor data is arranged in sequence. We propose a specialized DCNN to distinguish five phases in a gait cycle, based on IMU data and classified with foot switch information. The DCNN showed approximately 97% accuracy during an offline evaluation of gait phase recognition. Accuracy was highest in the swing phase and lowest in terminal stance.

The Synergistic Organization of Muscle Recruitment Constrains Visuomotor Adaptation

2009 ◽  
Vol 101 (5) ◽  
pp. 2263-2269 ◽  
Author(s):  
Aymar de Rugy ◽  
Mark R. Hinder ◽  
Daniel G. Woolley ◽  
Richard G. Carson
Keyword(s):  
Time Course ◽  
Neural Circuits ◽  
Visual Target ◽  
Sensory Information ◽  
Visuomotor Adaptation ◽  
Visuomotor Rotation ◽  
Joint Torques ◽  
Flexion Extension ◽  
Isometric Torque ◽  
The Individual

Reaching to visual targets engages the nervous system in a series of transformations between sensory information and motor commands. That which remains to be determined is the extent to which the processes that mediate sensorimotor adaptation to novel environments engage neural circuits that represent the required movement in joint-based or muscle-based coordinate systems. We sought to establish the contribution of these alternative representations to the process of visuomotor adaptation. To do so we applied a visuomotor rotation during a center-out isometric torque production task that involved flexion/extension and supination/pronation at the elbow-joint complex. In separate sessions, distinct half-quadrant rotations (i.e., 45°) were applied such that adaptation could be achieved either by only rescaling the individual joint torques (i.e., the visual target and torque target remained in the same quadrant) or by additionally requiring torque reversal at a contributing joint (i.e., the visual target and torque target were in different quadrants). Analysis of the time course of directional errors revealed that the degree of adaptation was lower (by ∼20%) when reversals in the direction of joint torques were required. It has been established previously that in this task space, a transition between supination and pronation requires the engagement of a different set of muscle synergists, whereas in a transition between flexion and extension no such change is required. The additional observation that the initial level of adaptation was lower and the subsequent aftereffects were smaller, for trials that involved a pronation–supination transition than for those that involved a flexion–extension transition, supports the conclusion that the process of adaptation engaged, at least in part, neural circuits that represent the required motor output in a muscle-based coordinate system.

scholarly journals Continuous Authentication of Automotive Vehicles Using Inertial Measurement Units

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5283 ◽  
Author(s):  
Gianmarco Baldini ◽  
Filip Geib ◽  
Raimondo Giuliani
Keyword(s):  
Wearable Sensors ◽  
Measurement Unit ◽  
Daily Routine ◽  
Inertial Measurement Units ◽  
Digital Output ◽  
Inertial Measurement ◽  
Continuous Authentication ◽  
Measurement Units ◽  
Analytical Approaches ◽  
Road Segments

The concept of Continuous Authentication is to authenticate an entity on the basis of a digital output generated in a continuous way by the entity itself. This concept has recently been applied in the literature for the continuous authentication of persons on the basis of intrinsic features extracted from the analysis of the digital output generated by wearable sensors worn by the subjects during their daily routine. This paper investigates the application of this concept to the continuous authentication of automotive vehicles, which is a novel concept in the literature and which could be used where conventional solutions based on cryptographic means could not be used. In this case, the Continuous Authentication concept is implemented using the digital output from Inertial Measurement Units (IMUs) mounted on the vehicle, while it is driving on a specific road path. Different analytical approaches based on the extraction of statistical features from the time domain representation or the use of frequency domain coefficients are compared and the results are presented for various conditions and road segments. The results show that it is possible to authenticate vehicles from the Inertial Measurement Unit (IMU) recordings with great accuracy for different road segments.

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