scholarly journals New Considerations for Collecting Biomechanical Data Using Wearable Sensors: How Does Inclination Influence the Number of Runs Needed to Determine a Stable Running Gait Pattern?

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2516 ◽  
Author(s):  
Nizam U. Ahamed ◽  
Lauren C. Benson ◽  
Christian A. Clermont ◽  
Andrew J. Pohl ◽  
Reed Ferber
Keyword(s):  
Wearable Sensors ◽  
Correlation Coefficients ◽  
Gait Pattern ◽  
Future Research ◽  
Stability Point ◽  
Testing Data ◽  
Measurement Units ◽  
Ground Contact Time ◽  
Stable Gait ◽  
Running Pattern

As inertial measurement units (IMUs) are used to capture gait data in real-world environments, guidelines are required in order to determine a ‘typical’ or ‘stable’ gait pattern across multiple days of data collection. Since uphill and downhill running can greatly affect the biomechanics of running gait, this study sought to determine the number of runs needed to establish a stable running pattern during level, downhill, and uphill conditions for both univariate and multivariate analyses of running biomechanical data collected using a single wearable IMU device. Pelvic drop, ground contact time, braking, vertical oscillation, pelvic rotation, and cadence, were recorded from thirty-five recreational runners running in three elevation conditions: level, downhill, and uphill. Univariate and multivariate normal distributions were estimated from differing numbers of runs and stability was defined when the addition of a new run resulted in less than a 5% change in the 2.5 and 97.5 quantiles of the 95% probability density function for each individual runner. This stability point was determined separately for each runner and each IMU variable (univariate and multivariate). The results showed that 2–4 runs were needed to define a stable running pattern for univariate, and 4–5 days were necessary for multivariate analysis across all inclination conditions. Pearson’s correlation coefficients were calculated to cross-validate differing elevation conditions and showed excellent correlations (r = 0.98 to 1.0) comparing the training and testing data within the same elevation condition and good to very good correlations (r = 0.63–0.88) when comparing training and testing data from differing elevation conditions. These results suggest that future research involving wearable technology should collect multiple days of data in order to build reliable and accurate representations of an individual’s stable gait pattern.

scholarly journals Estimation of Knee Joint Forces in Sport Movements Using Wearable Sensors and Machine Learning

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3690 ◽  
Author(s):  
Bernd J. Stetter ◽  
Steffen Ringhof ◽  
Frieder C. Krafft ◽  
Stefan Sell ◽  
Thorsten Stein
Keyword(s):  
Knee Joint ◽  
Wearable Sensors ◽  
Correlation Coefficients ◽  
Biomechanical Modelling ◽  
Joint Forces ◽  
Measurement Units ◽  
Artificial Neural Network Ann ◽  
The Right ◽  
Anterior Posterior ◽  
Good Agreement

Knee joint forces (KJF) are biomechanical measures used to infer the load on knee joint structures. The purpose of this study is to develop an artificial neural network (ANN) that estimates KJF during sport movements, based on data obtained by wearable sensors. Thirteen participants were equipped with two inertial measurement units (IMUs) located on the right leg. Participants performed a variety of movements, including linear motions, changes of direction, and jumps. Biomechanical modelling was carried out to determine KJF. An ANN was trained to model the association between the IMU signals and the KJF time series. The ANN-predicted KJF yielded correlation coefficients that ranged from 0.60 to 0.94 (vertical KJF), 0.64 to 0.90 (anterior–posterior KJF) and 0.25 to 0.60 (medial–lateral KJF). The vertical KJF for moderate running showed the highest correlation (0.94 ± 0.33). The summed vertical KJF and peak vertical KJF differed between calculated and predicted KJF across all movements by an average of 5.7% ± 5.9% and 17.0% ± 13.6%, respectively. The vertical and anterior–posterior KJF values showed good agreement between ANN-predicted outcomes and reference KJF across most movements. This study supports the use of wearable sensors in combination with ANN for estimating joint reactions in sports applications.

scholarly journals Validity of Wearable Sensors at the Shoulder Joint: Combining Wireless Electromyography Sensors and Inertial Measurement Units to Perform Physical Workplace Assessments

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1885 ◽  
Author(s):  
Isabelle Poitras ◽  
Mathieu Bielmann ◽  
Alexandre Campeau-Lecours ◽  
Catherine Mercier ◽  
Laurent J. Bouyer ◽  
...  
Keyword(s):  
Wearable Sensors ◽  
Correlation Coefficients ◽  
Emg Activity ◽  
Complex Task ◽  
Inertial Measurement Units ◽  
Simple Task ◽  
Inertial Measurement ◽  
Work Related ◽  
Measurement Units ◽  
Shoulder Kinematics

Background: Workplace adaptation is the preferred method of intervention to diminish risk factors associated with the development of work-related shoulder disorders. However, the majority of the workplace assessments performed are subjective (e.g., questionnaires). Quantitative assessments are required to support workplace adaptations. The aims of this study are to assess the concurrent validity of inertial measurement units (IMUs; MVN, Xsens) in comparison to a motion capture system (Vicon) during lifting tasks, and establish the discriminative validity of a wireless electromyography (EMG) system for the evaluation of muscle activity. Methods: Sixteen participants performed 12 simple tasks (shoulder flexion, abduction, scaption) and 16 complex lifting tasks (lifting crates of different weights at different heights). A Delsys Trigno EMG system was used to record anterior and middle deltoids’ EMG activity, while the Xsens and Vicon simultaneously recorded shoulder kinematics. Results: For IMUs, correlation coefficients were high (simple task: >0.968; complex task: >0.84) and RMSEs were low (simple task: <6.72°; complex task: <11.5°). For EMG, a significant effect of weight, height and a weight x height interaction (anterior: p < 0.001; middle: p < 0.03) were observed for RMS EMG activity. Conclusions: These results suggest that wireless EMG and IMUs are valid units that can be used to measure physical demand in workplace assessments.

scholarly journals The Relationship between VO2max, Power Management, and Increased Running Speed: Towards Gait Pattern Recognition through Clustering Analysis

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2422
Author(s):  
Juan Pardo Albiach ◽  
Melanie Mir-Jimenez ◽  
Vanessa Hueso Moreno ◽  
Iván Nácher Moltó ◽  
Javier Martínez-Gramage
Keyword(s):  
Stride Length ◽  
Wearable Sensors ◽  
Hierarchical Cluster ◽  
Gait Pattern ◽  
Maximum Oxygen Consumption ◽  
Heat Map ◽  
Higher Power ◽  
Cardiovascular Capacity ◽  
Ground Contact Time ◽  
The Relationship

Triathlon has become increasingly popular in recent years. In this discipline, maximum oxygen consumption (VO2max) is considered the gold standard for determining competition cardiovascular capacity. However, the emergence of wearable sensors (as Stryd) has drastically changed training and races, allowing for the more precise evaluation of athletes and study of many more potential determining variables. Thus, in order to discover factors associated with improved running efficiency, we studied which variables are correlated with increased speed. We then developed a methodology to identify associated running patterns that could allow each individual athlete to improve their performance. To achieve this, we developed a correlation matrix, implemented regression models, and created a heat map using hierarchical cluster analysis. This highlighted relationships between running patterns in groups of young triathlon athletes and several different variables. Among the most important conclusions, we found that high VO2max did not seem to be significantly correlated with faster speed. However, faster individuals did have higher power per kg, horizontal power, stride length, and running effectiveness, and lower ground contact time and form power ratio. VO2max appeared to strongly correlate with power per kg and this seemed to indicate that to run faster, athletes must also correctly manage their power.

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.

scholarly journals Detecting Toe-Off Events Utilizing a Vision-Based Method

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 329 ◽  
Author(s):  
Yunqi Tang ◽  
Zhuorong Li ◽  
Huawei Tian ◽  
Jianwei Ding ◽  
Bingxian Lin
Keyword(s):  
Wearable Sensors ◽  
Gait Pattern ◽  
Video Data ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Public Database ◽  
Video Frames ◽  
Different Types ◽  
Events Detection ◽  
Good Detection

Detecting gait events from video data accurately would be a challenging problem. However, most detection methods for gait events are currently based on wearable sensors, which need high cooperation from users and power consumption restriction. This study presents a novel algorithm for achieving accurate detection of toe-off events using a single 2D vision camera without the cooperation of participants. First, a set of novel feature, namely consecutive silhouettes difference maps (CSD-maps), is proposed to represent gait pattern. A CSD-map can encode several consecutive pedestrian silhouettes extracted from video frames into a map. And different number of consecutive pedestrian silhouettes will result in different types of CSD-maps, which can provide significant features for toe-off events detection. Convolutional neural network is then employed to reduce feature dimensions and classify toe-off events. Experiments on a public database demonstrate that the proposed method achieves good detection accuracy.

scholarly journals Psychometric Properties of the MyotonPRO in Dementia Patients with Paratonia

Gerontology ◽  
2017 ◽  
Vol 64 (4) ◽  
pp. 401-412 ◽  
Author(s):  
Hans Drenth ◽  
Sytse U. Zuidema ◽  
Wim P. Krijnen ◽  
Ivan Bautmans ◽  
Cees van der Schans ◽  
...  
Keyword(s):  
Psychometric Properties ◽  
Correlation Coefficient ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Functional Mobility ◽  
Future Research ◽  
Minimal Detectable Change ◽  
Muscle Properties ◽  
Dementia Patients ◽  
Longitudinal Outcome

Background: Paratonia is a distinctive form of hypertonia, causing loss of functional mobility in early stages of dementia to severe high muscle tone and pain in the late stages. For assessing and evaluating therapeutic interventions, objective instruments are required. Objective: Determine the psychometric properties of the MyotonPRO, a portable device that objectively measures muscle properties, in dementia patients with paratonia. Methods: Muscle properties were assessed with the MyotonPRO by 2 assessors within one session and repeated by the main researcher after 30 min and again after 6 months. Receiver operating characteristic curves were constructed for all MyotonPRO outcomes to discriminate between participants with (n = 70) and without paratonia (n = 82). In the participants with paratonia, correlation coefficients were established between the MyotonPRO outcomes and the Modified Ashworth Scale for paratonia (MAS-P) and muscle palpation. In participants with paratonia, reliability (intraclass correlation coefficient) and agreement values (standard error of measurement and minimal detectable change) were established. Longitudinal outcome from participants with paratonia throughout the study (n = 48) was used to establish the sensitivity for change (correlation coefficient) and responsiveness (minimal clinical important difference). Results: Included were 152 participants with dementia (mean [standard deviation] age of 83.5 [98.2]). The area under the curve ranged from 0.60 to 0.67 indicating the MyotonPRO is able to differentiate between participants with and without paratonia. The MyotonPRO explained 10-18% of the MAS-P score and 8-14% of the palpation score. Interclass correlation coefficients for interrater reliability ranged from 0.57 to 0.75 and from 0.54 to 0.71 for intrarater. The best agreement values were found for tone, elasticity, and stiffness. The change between baseline and 6 months in the MyotonPRO outcomes explained 8-13% of the change in the MAS-P scores. The minimal clinically important difference values were all smaller than the measurement error. Conclusion: The MyotonPRO is potentially applicable for cross-sectional studies between groups of paratonia patients and appears less suitable to measure intraindividual changes in paratonia. Because of the inherent variability in movement resistance in paratonia, the outcomes from the MyotonPRO should be interpreted with care; therefore, future research should focus on additional guidelines to increase the clinical interpretation and improving reproducibility.

Strategies to enhance compliance to physical activity for patients with insulin resistance

2007 ◽  
Vol 32 (3) ◽  
pp. 549-556 ◽  
Author(s):  
Alison Kirk ◽  
Pierpaolo De Feo
Keyword(s):  
Physical Activity ◽  
Insulin Resistance ◽  
Type 2 Diabetes ◽  
Wearable Sensors ◽  
Physical Activity Promotion ◽  
Physical Activity Behaviour ◽  
Future Research ◽  
Evidence Based ◽  
Activity Promotion

The evidence that physical activity is an effective therapeutic tool in the management of insulin resistance and type 2 diabetes is well documented. Limited research has addressed how best to promote and maintain physical activity in these individuals. This paper explores strategies to enhance compliance to physical activity for patients with insulin resistance. Several evidence-based guidelines and reviews recommend that physical activity interventions are based on a valid theoretical framework. However, there is no evidence-based consensus on the best theory or the combination of theories to use. Motivational tools such as pedometers, wearable sensors measuring energy expenditure, and point of choice prompts appear to be effective at stimulating short-term substantial increases in physical activity, but further strategies to maintain physical activity behaviour change are required. Physical activity consultation has demonstrated effective physical activity promotion over periods of up to 2 years in people with type 2 diabetes. Future research should identify the longer term effects of this intervention and the effectiveness of different methods of delivery. Overall, there needs to be a lot more focus on this area of research. Without this, the abundance of research investigating the effects of physical activity on people with insulin resistance and type 2 diabetes is essentially redundant.

Functional Behavior Assessment Rating Scales: Intrarater Reliability with Students with Emotional or Behavioral Disorders

2003 ◽  
Vol 28 (4) ◽  
pp. 386-400 ◽  
Author(s):  
Sally M. Barton-Arwood ◽  
Joseph H. Wehby ◽  
Philip L. Gunter ◽  
Kathleen L. Lane
Keyword(s):  
Behavioral Disorders ◽  
Rating Scales ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Functional Behavior Assessment ◽  
Behavior Assessment ◽  
Future Research ◽  
Intrarater Reliability ◽  
Functional Behavior ◽  
Emotional Or Behavioral Disorders

This study evaluated the intrarater reliability of two functional behavior assessment rating scales: the Motivation Assessment Scale and the Problem Behavior Questionnaire. Teachers rated 30 students from 10 self-contained classrooms for students with emotional or behavioral disorders on three separate occasions using both rating scales. Pearson correlation coefficients and exact and adjacent agreement percentages indicated variable and inconsistent ratings across administrations and rating scales. The authors discuss possible reasons for inconsistencies, as well as implications for practice and future research.

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