A computer vision approach for classifying isometric grip force exertion levels

Association between wrist-worn free-living accelerometry and hand grip strength in middle-aged and older adults

INTRODUCTION

Wrist-worn activity monitors have seen widespread adoption in recent times, particularly in young and sport-oriented cohorts, while their usage among older adults has remained relatively low. The main limitations are in regards to the lack of medical insights that current mainstream activity trackers can provide to older subjects. One of the most important research areas under investigation currently is the possibility of extrapolating clinical information from these wearable devices.

METHODS

The research question of this study is understanding whether accelerometry data collected for 7-days in free-living environments using a consumer-based wristband device, in conjunction with data-driven machine learning algorithms, is able to predict hand grip strength and possible conditions categorized by hand grip strength in a general population consisting of middle-aged and older adults.

RESULTS

The results of the regression analysis reveal that the performance of the developed models is notably superior to a simple mean-predicting dummy regressor. While the improvement in absolute terms may appear modest, the mean absolute error (6.32 kg for males and 4.53 kg for females) falls within the range considered sufficiently accurate for grip strength estimation. The classification models, instead, excel in categorizing individuals as frail/pre-frail, or healthy, depending on the T-score levels applied for frailty/pre-frailty definition. While cut-off values for frailty vary, the results suggest that the models can moderately detect characteristics associated with frailty (AUC-ROC: 0.70 for males, and 0.76 for females) and viably detect characteristics associated with frailty/pre-frailty (AUC-ROC: 0.86 for males, and 0.87 for females).

CONCLUSIONS

The results of this study can enable the adoption of wearable devices as an efficient tool for clinical assessment in older adults with multimorbidities, improving and advancing integrated care, diagnosis and early screening of a number of widespread diseases.

A systematic literature review of computer vision-based biomechanical models for physical workload estimation

Ergonomic risks, driven by strenuous physical demands in complex work settings, are prevalent across industries. Addressing these challenges through detailed assessment and effective interventions enhances safety and employee well-being. Proper and timely measurement of physical workloads is the initial step towards holistic ergonomic control. This study comprehensively explores existing computer vision-based biomechanical analysis methods for workload assessment, assessing their performance against traditional techniques, and categorising them for easier use. Recent strides in artificial intelligence have revolutionised workload assessment, especially in realistic work settings where conventional methods fall short. However, understanding the accuracy, characteristics, and practicality of computer vision-based methods versus traditional approaches remains limited. To bridge this knowledge gap, a literature review along with a meta-analysis was completed in this study to illuminate model accuracy, advantages, and challenges, offering valuable insights for refined technology implementation in diverse work environments.

Pinch force sense test-retest reliability evaluation using contralateral force matching task

A high test-retest reliability in measurement of pinch force sense is required to assess a clinical parameter accurately over a longitudinal study. Ipsilateral reproduction (IR) task and contralateral matching (CM) task have commonly been used for the assessment of force sense. To date, there has been little research on the test-retest reliability of pinch force sense utilizing the contralateral force matching task. This research aimed to explore this phenomenon across a spectrum of reference force levels (10, 30, and 50 percent maximum voluntary isometric contraction (MVIC)) using a contralateral matching task. Every participant in the study was tested twice by the same skilled experts, with each session separated by one week. Although normalized variable error indicated a poor level of reliability (intraclass correlation coefficient (ICC) = - 0.25 to 0.05) for these force sense tests, normalized constant error (ICC = 0.76-0.85) and normalized absolute error (ICC = 0.61-0.81) results indicated a fair to good of reliability. The lower bound of 95% CI of ICC for NAE and NCE indicated fair test-retest reliability (0.41-0.69). These findings suggest that investigators can reasonably obtain a fair to good test-retest reliability when investigating pinch force sense using the contralateral matching task. The Bland-Altman plots, SEM, and MDD95% were lower at these lower reference force level (10% MVIC) compared to the level of higher reference forces (30% and 50% MVIC). Therefore, when the reference force level increases, the participant needs a larger NAE or NCE decrease to show that their pinch force sense has indeed improved.

Soft computing applications in the field of human factors and ergonomics: A review of the past decade of research

The main objectives of this study were to 1) review the literature on the applications of soft computing concepts to the field of human factors and ergonomics (HFE) between 2013 and 2022 and 2) highlight future developments and trends. Multiple soft computing methods and techniques have been investigated for their ability to address various applications in HFE effectively. These techniques include fuzzy logic, artificial neural networks, genetic algorithms, and their combinations. Applications of these methods in HFE have been highlighted in one hundred and four articles selected from 406 papers. The results of this study help address the challenges of complexity, vagueness, and imprecision in human factors and ergonomics research through the application of soft computing methodologies.

Grip force makes wrist joint position sense worse

Background

The purpose of this study was to investigate how grip force affects wrist joint position sense.

Methods

Twenty-two healthy participants (11 men and 11 women) underwent an ipsilateral wrist joint reposition test at 2 distinct grip forces [0 and 15% of maximal voluntary isometric contraction (MVIC)] and 6 different wrist positions (pronation 24°, supination 24°, radial deviation 16°, ulnar deviation 16°, extension 32°, and flexion 32°).

Results

The findings demonstrated significantly elevated absolute error values at 15% MVIC (3.8 ± 0.3°) than at 0% MVIC grip force [3.1 ± 0.2°, t(20) = 2.303, P = 0.032].

Conclusion

These findings demonstrated that there was significantly worse proprioceptive accuracy at 15% MVIC than at 0% MVIC grip force. These results may contribute to a better comprehension of the mechanisms underlying wrist joint injuries, the development of preventative measures to lower the risk of injuries, and the best possible design of engineering or rehabilitation devices.

Influence of an armrest support on handgrip strength in different arm and shoulder flexion angles in overhead postures

A study was undertaken in which the handgrip strength in three arm positions above the shoulder was measured to compare handgrip strength when arm support is used and when it is not used. Grip forces were generated in pairs of flexion angles, corresponding to shoulder and elbow at 90°-90°, 135°-45° and 160°-20°. Thirty-two participants completed the present study; 23 men and nine women with a median age of 23.1 (SD ±3.6) years. A manual handgrip dynamometer (0-90 kg) and an adjustable angle arm support (AAAS) were used during the data collection. Two-way analysis of variance (ANOVA) for repeated measurements indicates a significant effect of the AAAS factor on the handgrip strength, as well as on the AAAS × angle interaction. However, there is no significant effect of the angle factor on the AAAS × angle interaction.

The effect of pinch span on pinch force sense in healthy participants

The purpose of the current investigation was to evaluate the effect of pinch span on the perception of pinch force in typical participants. The healthy participants (10 males and 10 females) conducted an ipsilateral force reproduction test with three distinct pinch spans (2, 4, and 6 cm) at three distinct forces of 10%, 30%, and 50% maximum voluntary isometric contraction. The findings revealed a significantly greater consistency (lower variable error (VE)) of 4 cm compared with 2 and 6 cm pinch spans. Our study also showed that the participants might use a larger force (more overestimated) output for larger pinch spans (4 and 6 cm) than small pinch spans (2 cm). These results may offer significant insights into the higher rates of musculoskeletal disorders among females, enabling researchers and clinicians to design novel interventions and tools to improve pinch force perception and reduce hand injury rates in males and females.

Investigating the Use of Changes in Facial Features as Indicators of Physical Workload

OCCUPATIONAL APPLICATIONSPhysical workload may lead to negative outcomes, including musculoskeletal disorders. In this study, we found that there were observable changes in facial features over the length of a low intensity, prolonged assembly task, and that these changes were correlated to other measures of physical workload. This method can be implemented by practitioners to evaluate physical workload.

Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature

Physical ergonomics has established itself as a valid strategy for monitoring potential disorders related, for example, to working activities. Recently, in the field of physical ergonomics, several studies have also shown potential for improvement in experimental methods of ergonomic analysis, through the combined use of artificial intelligence, and wearable sensors. In this regard, this review intends to provide a first account of the investigations carried out using these combined methods, considering the period up to 2021. The method that combines the information obtained on the worker through physical sensors (IMU, accelerometer, gyroscope, etc.) or biopotential sensors (EMG, EEG, EKG/ECG), with the analysis through artificial intelligence systems (machine learning or deep learning), offers interesting perspectives from both diagnostic, prognostic, and preventive points of view. In particular, the signals, obtained from wearable sensors for the recognition and categorization of the postural and biomechanical load of the worker, can be processed to formulate interesting algorithms for applications in the preventive field (especially with respect to musculoskeletal disorders), and with high statistical power. For Ergonomics, but also for Occupational Medicine, these applications improve the knowledge of the limits of the human organism, helping in the definition of sustainability thresholds, and in the ergonomic design of environments, tools, and work organization. The growth prospects for this research area are the refinement of the procedures for the detection and processing of signals; the expansion of the study to assisted working methods (assistive robots, exoskeletons), and to categories of workers suffering from pathologies or disabilities; as well as the development of risk assessment systems that exceed those currently used in ergonomics in precision and agility.

Assessment of grip force sense test-retest reliability in healthy male participants

There has been a lack of research to date regarding the test-retest reliability of grip force sense in healthy adult males. This study was therefore designed to explore this topic across a series of target force levels using an ipsilateral force reproduction task. The same experienced research staff conducted two testing sessions for each study participant, with 1 week between test sessions. Intraclass correlation coefficient values indicated that these force sensing tests exhibited good to fair reliability with respect to both absolute error (0.42-0.63) and constant error (0.49-0.60), although variable error was indicative of poor reliability (-0.85 to 0.14). Together, these results suggest that researchers can achieve a fair level of test-retest reliability when analysing grip force sense in healthy adult males, with results being most reliable at force levels of 20 N and 50 N, as determined based upon measured constant error and absolute error. Practitioner summary: To ensure that grip force sense can be accurately interpreted over time, it is important to assess the test-retest reliability. It is recommended that practitioners measure the absolute error and constant error at force levels of 20 N and 50 N when assessing grip force sense in a clinical setting.

Exploring Sex Differences and Force Level Effects on Grip Force Perception in Healthy Adults

This study aimed to explore the effect of sex and force level on grip force reproduction in healthy adults by conducting a force reproduction task. Participants (n = 28) were instructed to replicate a range of reference grip force levels (10-130 N in 10 N increments). We found that women (absolute error: 16.2 ± 8.7 N) replicated these force levels more accurately than men (absolute error: 23.1 ± 9.5 N) at higher force levels (90-130 N). Furthermore, the force reproductions were most accurate at the 30-50 N range for men and the 50-60 N range for women. These results may offer significant insights into the higher rates of musculoskeletal disorders among women, enabling researchers and clinicians to design novel interventions and tools that can improve grip force perception and reduce hand injury rates in both men and women.

The role of machine learning in the primary prevention of work-related musculoskeletal disorders: A scoping review

To determine the applications of machine learning (ML) techniques used for the primary prevention of work-related musculoskeletal disorders (WMSDs), a scoping review was conducted using seven literature databases. Of the 4,639 initial results, 130 primary research studies were deemed relevant for inclusion. Studies were reviewed and classified as a contribution to one of six steps within the primary WMSD prevention research framework by van der Beek et al. (2017). ML techniques provided the greatest contributions to the development of interventions (48 studies), followed by risk factor identification (33 studies), underlying mechanisms (29 studies), incidence of WMSDs (14 studies), evaluation of interventions (6 studies), and implementation of effective interventions (0 studies). Nearly a quarter (23.8%) of all included studies were published in 2020. These findings provide insight into the breadth of ML techniques used for primary WMSD prevention and can help identify areas for future research and development.

A deep learning-based method for grip strength prediction: Comparison of multilayer perceptron and polynomial regression approaches

The objective of this study was to accurately predict the grip strength using a deep learning-based method (e.g., multi-layer perceptron [MLP] regression). The maximal grip strength with varying postures (upper arm, forearm, and lower body) of 164 young adults (100 males and 64 females) were collected. The data set was divided into a training set (90% of data) and a test set (10% of data). Different combinations of variables including demographic and anthropometric information of individual participants and postures was tested and compared to find the most predictive model. The MLP regression and 3 different polynomial regressions (linear, quadratic, and cubic) were conducted and the performance of regression was compared. The results showed that including all variables showed better performance than other combinations of variables. In general, MLP regression showed higher performance than polynomial regressions. Especially, MLP regression considering all variables achieved the highest performance of grip strength prediction (RMSE = 69.01N, R = 0.88, ICC = 0.92). This deep learning-based regression (MLP) would be useful to predict on-site- and individual-specific grip strength in the workspace to reduce the risk of musculoskeletal disorders in the upper extremity.