Visualizing stressful aspects of repetitive motion tasks and opportunities for ergonomic improvements using computer vision

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.

Assessment of work-related hand and elbow workloads using measurement-based TLV for HAL

Direct-measurement-based methods for assessing workloads of the hand or elbow in the field are rare. Aim of the study was to develop such a method based on the Threshold Limit Value for Hand Activity Level (TLV for HAL). Hence, HAL was quantified using kinematic data (mean power frequencies, angular velocities and micro-pauses) and combined with electromyographic data (root-mean-square values) in order to generate a measurement-based TLV for HAL (mTLV for HAL). The multi-sensor system CUELA including inertial sensors, potentiometers and a 4-channel surface electromyography module was used. For wrist and elbow regions, associations between mTLV for HAL and disorders/complaints (quantified by odds ratios (OR [95%-confidence interval])) were tested exploratively within a cross-sectional field study with 500 participants. Higher workloads were frequently significantly associated with arthrosis of distal joints (9.23 [3.29-25.87]), wrist complaints (2.89 [1.63-5.11]) or elbow complaints (1.99 [1.08-3.67]). The new method could extend previous application possibilities.

Comparisons of physical exposure between workers harvesting apples on mobile orchard platforms and ladders, part 2: Repetitive upper arm motions

Farmworkers are exposed to physical risk factors including repetitive motions. Existing ergonomic assessment methods are primarily laboratory-based and, thus, inappropriate for use in the field. This study presents an approach to characterize the repetitive motions of the upper arms based on direct measurement using accelerometers. Repetition rates were derived from upper arm inclination data and with video recordings in the field. This method was used to investigate whether harvesting with mobile platforms (teams harvesting apples from the platform and the ground) increased the farmworkers' exposure to upper arm repetitive motions compared to traditional harvesting using ladders. The ladder workers had higher repetitive motions (13.7 cycles per minute) compared to the platform and ground workers (11.7 and 12.2 cycles per minutes). The higher repetitions in the ladder workers were likely due to their ability to work independently and the additional arm movements associated with ladder climbing and walking.

A computer vision approach for classifying isometric grip force exertion levels

Exposure to high and/or repetitive force exertions can lead to musculoskeletal injuries. However, measuring worker force exertion levels is challenging, and existing techniques can be intrusive, interfere with human-machine interface, and/or limited by subjectivity. In this work, computer vision techniques are developed to detect isometric grip exertions using facial videos and wearable photoplethysmogram. Eighteen participants (19-24 years) performed isometric grip exertions at varying levels of maximum voluntary contraction. Novel features that predict forces were identified and extracted from video and photoplethysmogram data. Two experiments with two (High/Low) and three (0%MVC/50%MVC/100%MVC) labels were performed to classify exertions. The Deep Neural Network classifier performed the best with 96% and 87% accuracy for two- and three-level classifications, respectively. This approach was robust to leave subjects out during cross-validation (86% accuracy when 3-subjects were left out) and robust to noise (i.e. 89% accuracy for correctly classifying talking activities as low force exertions). Practitioner summary: Forceful exertions are contributing factors to musculoskeletal injuries, yet it remains difficult to measure in work environments. This paper presents an approach to estimate force exertion levels, which is less distracting to workers, easier to implement by practitioners, and could potentially be used in a wide variety of workplaces. Abbreviations: MSD: musculoskeletal disorders; ACGIH: American Conference of Governmental Industrial Hygienists; HAL: hand activity level; MVC: maximum voluntary contraction; PPG: photoplethysmogram; DNN: deep neural networks; LOSO: leave-one-subject-out; ROC: receiver operating characteristic; AUC: area under curve.

Interactive ergonomic risk mapping: a practical approach for visual management of workplace ergonomics

Current studies identify an increasing need to develop enriched tools for ergonomic risk management that can foster an atmosphere enhancing commitment of all stakeholders to create a safe and heathy work environment using ergonomic principles. In this study, a new tool for visualization of ergonomic practices in the workplace is proposed. For developing this tool, an interactive ergonomic risk mapping (intERM) methodology is introduced consisting of five steps while integrating the company's strategic vision and helping to accommodate the impacts of changes in policy and regulatory context, economic and demographic environment, technology and employment context. The proposed systematic and practical methodology is demonstrated on a real-life example. This visual and interactive tool enables prompt identification of and reaction to ergonomic risks, anticipating changes for reducing/eliminating ergonomic risks, as well as increasing company-wide awareness for ergonomic risks and enhancing engagement and ownership of stakeholders.

Using Surgeon Hand Motions to Predict Surgical Maneuvers

OBJECTIVE

This study explores how common machine learning techniques can predict surgical maneuvers from a continuous video record of surgical benchtop simulations.

BACKGROUND

Automatic computer vision recognition of surgical maneuvers (suturing, tying, and transition) could expedite video review and objective assessment of surgeries.

METHOD

We recorded hand movements of 37 clinicians performing simple and running subcuticular suturing benchtop simulations, and applied three machine learning techniques (decision trees, random forests, and hidden Markov models) to classify surgical maneuvers every 2 s (60 frames) of video.

RESULTS

Random forest predictions of surgical video correctly classified 74% of all video segments into suturing, tying, and transition states for a randomly selected test set. Hidden Markov model adjustments improved the random forest predictions to 79% for simple interrupted suturing on a subset of randomly selected participants.

CONCLUSION

Random forest predictions aided by hidden Markov modeling provided the best prediction of surgical maneuvers. Training of models across all users improved prediction accuracy by 10% compared with a random selection of participants.

APPLICATION

Marker-less video hand tracking can predict surgical maneuvers from a continuous video record with similar accuracy as robot-assisted surgical platforms, and may enable more efficient video review of surgical procedures for training and coaching.

Simulating the influence of physical overload on assembly line performance: A case study in an automotive electrical component plant

Although the workstations of a Brazilian automotive electrical harness production line are set close to TAKT time (the production rate required to meet demand), factory performance is compromised regarding: (i) sick leaves due to occupational disease (105 employees last year) and (ii) a production rate at only 42% of capacity. Our objective was to simulate the performance of a production line balanced against physical overload by the addition of an extra workstation. Based on ergonomic work analysis, the study applied System Dynamics at the global observation stage to obtain a systemic interpretation of the factors involved in production line performance. According to the indicators, the alternative configuration reduced physical overload by 36%, which would result in a sick leave rate of 50.8 employees/year (51.6% lower than the current configuration), as well as a production rate at 99% of capacity (a 92.7% increase over the current configuration). We found that reducing physical overload allows the "workforce control" loop to govern the system, producing favorable results. We conclude that setting the work cycle overly close to TAKT time leads to overload, due to the shorter recovery times at the end of each cycle. Thus, it is necessary to seek a balance between efficiency gains through downtime reduction and the physiological recovery of workers.

Predicting Sagittal Plane Lifting Postures From Image Bounding Box Dimensions

OBJECTIVE

A method for automatically classifying lifting postures from simple features in video recordings was developed and tested. We explored if an "elastic" rectangular bounding box, drawn tightly around the subject, can be used for classifying standing, stooping, and squatting at the lift origin and destination.

BACKGROUND

Current marker-less video tracking methods depend on a priori skeletal human models, which are prone to error from poor illumination, obstructions, and difficulty placing cameras in the field. Robust computer vision algorithms based on spatiotemporal features were previously applied for evaluating repetitive motion tasks, exertion frequency, and duty cycle.

METHODS

Mannequin poses were systematically generated using the Michigan 3DSSPP software for a wide range of hand locations and lifting postures. The stature-normalized height and width of a bounding box were measured in the sagittal plane and when rotated horizontally by 30°. After randomly ordering the data, a classification and regression tree algorithm was trained to classify the lifting postures.

RESULTS

The resulting tree had four levels and four splits, misclassifying 0.36% training-set cases. The algorithm was tested using 30 video clips of industrial lifting tasks, misclassifying 3.33% test-set cases. The sensitivity and specificity, respectively, were 100.0% and 100.0% for squatting, 90.0% and 100.0% for stooping, and 100.0% and 95.0% for standing.

CONCLUSIONS

The tree classification algorithm is capable of classifying lifting postures based only on dimensions of bounding boxes.

APPLICATIONS

It is anticipated that this practical algorithm can be implemented on handheld devices such as a smartphone, making it readily accessible to practitioners.