EMG-Based Classification of Forearm Muscles in Prehension Movements: Performance Comparison of Machine Learning Algorithms

Rahman, Sabuktagin; Altwijri, Omar; Ali, Md. Asraf

doi:10.1007/978-3-030-52856-0_24

Cited by 2 publications

(2 citation statements)

References 43 publications

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“…Utilization of multiple automated responses from other independent ergonomic transducers, i.e., BMI, HR, EER and EMG-RMS, also aided in minimizing subjectivity and enhancing accuracy. Earlier studies have determined overall workload classes solely based on RMS responses and obtained slightly lower or similar accuracies using RFC machine learning [ 43 , 59 ]. RFC performed well compared to other machine learning algorithms ( Table 4 ) because it uses a bagging approach to create a bunch of decision trees with a random subset of the data and then trains the model several times on a random sample to achieve good prediction performance.…”

Section: Discussionmentioning

confidence: 99%

“…KNN is the simplest classifier that computes the distance of data points from the neighbors (k) and classifies them into different classes [ 41 , 42 ]. RFC uses a bagging approach to create several decision trees (n_estimators) where each node questions a datapoint, and the branches represent possible answers to that question [ 43 ]. SVM is a supervised machine learning classifier that classifies data into different domains by finding hyperplanes with a maximum margin [ 42 ].…”

Section: Methodsmentioning

confidence: 99%

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Workload Assessment of Tractor Operations with Ergonomic Transducers and Machine Learning Techniques

Hota

Tewari

Chandel

2023

Sensors

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Dynamic muscular workload assessments of tractor operators are rarely studied or documented, which is critical to improving their performance efficiency and safety. A study was conducted to assess and model dynamic load on muscles, physiological variations, and discomfort of the tractor operators arriving from the repeated clutch and brake operations using wearable non-invasive ergonomic transducers and data-run techniques. Nineteen licensed tractor operators operated three different tractor types of varying power ranges at three operating speeds (4–5 km/h), and on two common operating surfaces (tarmacadam and farm roads). During these operations, ergonomic transducers were utilized to capture the load on foot muscles (gastrocnemius right [GR] and soleus right [SR] for brake operation and gastrocnemius left [GL], and soleus left [SL] for clutch operation) using electromyography (EMG). Forces exerted by the feet during brake and clutch operations were measured using a custom-developed foot transducer. During the process, heart rate (HR) and oxygen consumption rates (OCR) were also measured using HR monitor and K4b2 systems, and energy expenditure rate (EER) was determined using empirical equation. Post-tractor operation cycle, an overall discomfort rating (ODR) for that operation was manually recorded on a 10-point psychophysical scale. EMG-based maximum volumetric contraction (%MVC) measurements revealed higher strain on GR (%MVC = 43%), GL (%MVC = 38%), and SR (%MVC = 41%) muscles which in normal conditions should be below 30%. The clutch and brake actuation forces were recorded in the ranges of 90–312 N and 105–332 N, respectively and were significantly affected by the operating speed, tractor type, and operating surface (p < 0.05). EERs of the operators were measured in the moderate-heavy to heavy ranges (9–24 kJ/min) during the course of trials, suggesting the need to refine existing clutch and brake system designs. Average operator ODR responses indicated 7.8% operations in light, 48.5% in light-moderate, 25.2% in moderate, 10.7% in moderate-high, and 4.9% operations in high discomfort categories. When evaluated for the possibility of minimizing the number of transducers for physical workload assessment, EER showed moderate-high correlations with the EMG signals (rGR = 0.78, rGL = 0.75, rSR = 0.68, rSL = 0.66). Similarly, actuation forces had higher correlations with EMG signals for all the selected muscles (r = 0.70–0.87), suggesting the use of simpler transducers for effective operator workload assessment. As a means to minimize subjectivity in ODR responses, machine learning algorithms, including K-nearest neighbor (KNN), random forest classifier (RFC), and support vector machine (SVM), predicted the ODR using body mass index (BMI), HR, EER, and EMG at high accuracies of 87–97%, with RFC being the most accurate. Such high-throughput and data-run ergonomic evaluations can be instrumental in reconsidering workplace designs and better fits for end-users in terms of agricultural tractors and machinery systems.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Workload Assessment of Tractor Operations with Ergonomic Transducers and Machine Learning Techniques

Hota

Tewari

Chandel

2023

Sensors

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A Machine Learning–Based Ergonomic Assessment of Wireless Hand Control System for Lower‐Limb Disabled Tractor Operators and Abled Female Agricultural Workers

Hota,

Tewari

2024

Journal of Field Robotics

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Tractor being the most used power source for agricultural operations needs hand control (HC) and foot control (FC) to maneuver it. FCs restrict lower‐limb disabled agricultural workers from participating in tractor operation, and high requirement of actuation forces to operate FCs may create overexertion and early fatigue to female agricultural workers. Therefore, a sensor‐based HC system has been developed to assist them in tractor operation with minimal actuating force. This study focuses on ergonomic assessment of the HC system to assess the suitability for the abled and disabled agricultural workers, including physiological, psychophysical, and muscle fatigue parameters. Heart rate (HR) of abled male and female, and disabled male and female was observed in the range of 83–118, 85–117, 93–118, and 92–114 beats/min, respectively, during tractor operation. Energy expenditure rate (EER) during tractor operation with FCs (9.7–17.4 kJ/min) was observed higher than with the HC system (7.3–16.5 kJ/min). Body parts discomfort was observed highest for the right hand of all the subjects (4.9–5.3) and maximum overall discomfort was experienced by abled females during the operation with FCs (5.4) as they have to exert higher force. The root mean square (RMS) value of the electromyography signal obtained for extensor digitorum muscle was found to be higher for all the subjects and with both HC and FC (abled male, 17.37–40.43 µV; abled female, 14.76–45.29 µV; disabled male, 15.49–40.23 µV; disabled female, 30.32–54.29 µV) than other upper arm muscles middle deltoid, flexor carpi radialis, and brachioradialis. Muscle workload for all the selected muscles of all the subjects was observed within the recommended limit during the tractor operation with a developed HC system (< 30%). Categorization of overall discomfort rating (ODR) of the subjects using HR, EER, and RMS through machine learning algorithms such as k‐nearest neighbor (KNN), random forest classifier, and support vector machine predicted the ODR with accuracies in the range of 77%–83%. KNN algorithm was found to be most accurate with prediction accuracy of 83%. The developed HC system provides assistantship to the lower‐limb disabled agricultural workers (1%–100% disability of lower limbs) and allows female workers to operate the tractor with minimal physical exertion.

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EMG-Based Classification of Forearm Muscles in Prehension Movements: Performance Comparison of Machine Learning Algorithms

Cited by 2 publications

References 43 publications

Workload Assessment of Tractor Operations with Ergonomic Transducers and Machine Learning Techniques

Workload Assessment of Tractor Operations with Ergonomic Transducers and Machine Learning Techniques

A Machine Learning–Based Ergonomic Assessment of Wireless Hand Control System for Lower‐Limb Disabled Tractor Operators and Abled Female Agricultural Workers

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