K-score: A novel scoring system to quantify fatigue-related ergonomic risk based on joint angle measurements via wearable inertial measurement units

Work-related musculoskeletal disorders have been recognized as a global problem that affects millions of people annually. Fatigue is one of the main contributors to musculoskeletal disorders. Thus, this study investigated fatigue detection based on the measured body motion by wearable inertial measurement units. We quantified the body motion during manual handling tasks using a novel kinematic score (i.e., K-score), and the Rapid Entire Body Assessment (REBA). K-score and REBA were calculated using joint angles. Nevertheless, unlike REBA, K-score showed a significant correlation (Spearman's correlation coefficient of ρ(302) = 0.21, p < 0.05) with electromyography (EMG) signal amplitude, which was affected by muscle fatigue. Therefore, in-field measurement of K-score using inertial measurement units could detect the fatigue-induced change of body motion in long-duration manual handling tasks. Our proposed K-score can be used to assess fatigue-related ergonomic risk in long-term and real-world working conditions without the need for tedious EMG recording at workplaces.

IMU, sEMG, or their cross-correlation and temporal similarities: Which signal features detect lateral compensatory balance reactions more accurately?

BACKGROUND AND OBJECTIVE

Falls are the leading cause of fatal and non-fatal injuries among seniors worldwide. While laboratory evidence supports the view that impaired ability to execute compensatory balance responses (CBRs) is linked to an increased risk of falling, existing unsupervised fall risk assessment methods are mainly focused on detecting changes in spatio-temporal gait parameters over time rather than naturally-occurring CBR events. To address the gap in available methods, this paper compares the capability of machine learning-based models trained on the kinematic data from inertial measurement units (IMU) and surface electromyography (sEMG) features to detect lateral CBRs, to ultimately address detection of CBRs in free-living conditions. Moreover, we propose a novel "Hybrid" feature set, which considers cross-correlation and temporal similarities between the normalized kinematic and sEMG signals.

METHODS

Focusing on frontal plane perturbations, a classifier to automatically: 1) detect lateral CBRs during normal gait, and 2) identify type (i.e., crossover, sidestep) using data from three wearable IMUs and 4 sEMG signals from the thigh (i.e., biceps femoris, rectus femoris) and lower leg muscles (i.e., gastrocnemious, tibialis anterior) was developed. In total, 600 trials (including 358 lateral CBRs) from 7 young, healthy adults were analyzed. The effects of feature type (IMU, sEMG, Hybrid) and sensor placement on the random forest-based classifier performance were investigated.

RESULTS

CBR detection (i.e., CBR vs normal gait) accuracies (leave-one-subject-out cross validation) were 83.95% and 99.21% using sEMG-based and IMU-based features, respectively, which dropped to 72.17% and 84.83% for the multiclass identification (i.e., side-step vs cross-over vs normal gait) problem. Findings yielded shank as the best overall location for the multiclass problem, and chest as the most accurate for CBR detection. In general, adding sEMG and Hybrid features to IMUs yielded incremental improvements in CBR detection and type identification (87.03% leave-one-subject-out cross-validation for type identification).

CONCLUSION

The findings of this study demonstrate that IMU-based features are favourable over sEMG and Hybrid features for the task of CBR detection, with incremental value for type identification. Evidence presented suggests that Hybrid features may increase performance for other wearable sensor applications (e.g. activity recognition systems).

The effect of levodopa on postural stability evaluated by wearable inertial measurement units for idiopathic and vascular Parkinson's disease

BACKGROUND

Postural stability analysis has shown that postural control is impaired in untreated idiopathic Parkinson's disease (IPD), even in the early stages of the disease. Vascular Parkinson's disease (VPD) lacks consensus clinical criteria or diagnostic tests. Moreover, the levodopa effect on postural balance remains undefined for IPD and even less so for VPD.

OBJECTIVE

To characterize postural stability, using kinematic analysis with wearable inertial measurement units, in IPD and VPD patients without clinical PI, and to subsequently analyze the response to levodopa.

METHODS

Ten patients with akinetic-rigid IPD and five patients with VPD were included. Clinical and postural stability kinematic analysis was performed before and after levodopa challenge, on different standing tasks: normal stance (NS), Romberg eyes open (REO) and Romberg eyes closed.

RESULTS

In the "off state", VPD patients had higher mean distances and higher maximal distance of p ostural sway on NS and REO tasks, respectively. VPD patients maintained a higher range of anterior-posterior (AP) postural sway after levodopa. In the absence of PI and non-significant differences in UPDRS-III, a higher mPIGD score in the VPD patients was mainly due to gait disturbance. Gait disturbance, and not UPDRS-III, influenced the degree of postural sway response to levodopa for VPD patients.

CONCLUSION

Quantitative postural sway evaluation is useful in the investigation of Parkinsonian syndromes. VPD patients have higher AP postural sway that is correlated with their gait disturbance burden and also not responsive to levodopa. These observations corroborate the interconnection of postural control and locomotor networks.