No abstract

Overhead work is an important risk factor for upper extremity (UE) musculoskeletal disorders. We examined the potential of a mechanical arm and an exoskeletal vest as a wearable assistive device (WADE) for overhead work. Twelve participants completed 10 minutes of simulated, intermittent overhead work, using each of three payloads (1.1, 3.4 and 8.1 kg) and with/without the WADE. Ratings of perceived discomfort (RPDs) and electromyography (EMG) were obtained for the upper arms, shoulders and low back. Using the WADE, UE RPDs decreased by ∼50% with the heavier payloads, whereas smaller (∼25%) and non-significant increases in low-back RPDs were found and were relatively independent of payload. Changes in RPDs with WADE use were consistent with physical demands indicated by EMG, though EMG-based differences in fatigue were less apparent. Participants generally preferred using the WADE, particularly with heavier payloads. These results supported the potential utility of a WADE as an intervention for overhead work.

No abstract

Nonspecific low back pain (NSLBP) constitutes a critical health challenge that impacts millions of people worldwide with devastating health and socioeconomic consequences. In today’s clinical settings, practitioners continue to follow conventional guidelines to categorize NSLBP patients based on subjective approaches, such as the STarT Back Screening Tool (SBST). This study aimed to develop a sensor-based machine learning model to classify NSLBP patients into different subgroups according to quantitative kinematic data, i.e., trunk motion and balance-related measures, in conjunction with STarT output. Specifically, inertial measurement units (IMU) were attached to the trunks of ninety-four patients while they performed repetitive trunk flexion/extension movements on a balance board at self-selected pace. Machine learning algorithms (support vector machine (SVM) and multi-layer perceptron (MLP)) were implemented for model development, and SBST results were used as ground truth. The results demonstrated that kinematic data could successfully be used to categorize patients into two main groups: high vs. low-medium risk. Accuracy levels of ~75% and 60% were achieved for SVM and MLP, respectively. Additionally, among a range of variables detailed herein, time-scaled IMU signals yielded the highest accuracy levels (i.e., ~75%). Our findings support the improvement and use of wearable systems in developing diagnostic and prognostic tools for various healthcare applications. This can facilitate development of an improved, cost-effective quantitative NSLBP assessment tool in clinical and home settings towards effective personalized rehabilitation.

No abstract