Dataset Information

Estimation of joint torque in dynamic activities using wearable A-mode ultrasound.

ABSTRACT: The human body constantly experiences mechanical loading. However, quantifying internal loads within the musculoskeletal system remains challenging, especially during unconstrained dynamic activities. Conventional measures are constrained to laboratory settings, and existing wearable approaches lack muscle specificity or validation during dynamic movement. Here, we present a strategy for estimating corresponding joint torque from muscles with different architectures during various dynamic activities using wearable A-mode ultrasound. We first introduce a method to track changes in muscle thickness using single-element ultrasonic transducers. We then estimate elbow and knee torque with errors less than 7.6% and coefficients of determination (R²) greater than 0.92 during controlled isokinetic contractions. Finally, we demonstrate wearable joint torque estimation during dynamic real-world tasks, including weightlifting, cycling, and both treadmill and outdoor locomotion. The capability to assess joint torque during unconstrained real-world activities can provide new insights into muscle function and movement biomechanics, with potential applications in injury prevention and rehabilitation.

SUBMITTER: Jin Y

PROVIDER: S-EPMC11233567 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Estimation of joint torque in dynamic activities using wearable A-mode ultrasound.

Jin Yichu Y Alvarez Jonathan T JT Suitor Elizabeth L EL Swaminathan Krithika K Chin Andrew A Civici Umut S US Nuckols Richard W RW Howe Robert D RD Walsh Conor J CJ

Nature communications 20240709 1

The human body constantly experiences mechanical loading. However, quantifying internal loads within the musculoskeletal system remains challenging, especially during unconstrained dynamic activities. Conventional measures are constrained to laboratory settings, and existing wearable approaches lack muscle specificity or validation during dynamic movement. Here, we present a strategy for estimating corresponding joint torque from muscles with different architectures during various dynamic activi ...[more]

PMID: 38982087

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Project description:High-density electromyography (HD-EMG) can provide a natural interface to enhance human-computer interaction (HCI). This study aims to demonstrate the capability of a novel HD-EMG forearm sleeve equipped with up to 150 electrodes to capture high-resolution muscle activity, decode complex hand gestures, and estimate continuous hand position via joint angle predictions. Ten able-bodied participants performed 37 hand movements and grasps while EMG was recorded using the HD-EMG sleeve. Simultaneously, an 18-sensor motion capture glove calculated 23 joint angles from the hand and fingers across all movements for training regression models. For classifying across the 37 gestures, our decoding algorithm was able to differentiate between sequential movements with 97.3±0.3% accuracy calculated on a 100 ms bin-by-bin basis. In a separate mixed dataset consisting of 19 movements randomly interspersed, decoding performance achieved an average bin-wise accuracy of 92.8±0.8% . When evaluating decoders for use in real-time scenarios, we found that decoders can reliably decode both movements and movement transitions, achieving an average accuracy of 93.3±0.9% on the sequential set and 88.5±0.9% on the mixed set. Furthermore, we estimated continuous joint angles from the EMG sleeve data, achieving a R2 of 0.884±0.003 in the sequential set and 0.750±0.008 in the mixed set. Median absolute error (MAE) was kept below 10° across all joints, with a grand average MAE of 1.8±0.04∘ and 3.4±0.07∘ for the sequential and mixed datasets, respectively. We also assessed two algorithm modifications to address specific challenges for EMG-driven HCI applications. To minimize decoder latency, we used a method that accounts for reaction time by dynamically shifting cue labels in time. To reduce training requirements, we show that pretraining models with historical data provided an increase in decoding performance compared with models that were not pretrained when reducing the in-session training data to only one attempt of each movement. The HD-EMG sleeve, combined with sophisticated machine learning algorithms, can be a powerful tool for hand gesture recognition and joint angle estimation. This technology holds significant promise for applications in HCI, such as prosthetics, assistive technology, rehabilitation, and human-robot collaboration.

Dataset Information

Estimation of joint torque in dynamic activities using wearable A-mode ultrasound.

Publications

Estimation of joint torque in dynamic activities using wearable A-mode ultrasound.

Similar Datasets

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