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Loss-of-balance (LOB) events, such as trips and slips, are frequent among community-dwelling older adults and are an indicator of increased fall risk. In a preliminary study, eight community-dwelling older adults with a history of falls were asked to perform everyday tasks in the real world while donning a set of three inertial measurement sensors (IMUs) and report LOB events via a voice-recording device. Over 290 h of real-world kinematic data were collected and used to build and evaluate classification models to detect the occurrence of LOB events. Spatiotemporal gait metrics were calculated, and time stamps for when LOB events occurred were identified. Using these data and machine learning approaches, we built classifiers to detect LOB events. Through a leave-one-participant-out validation scheme, performance was assessed in terms of the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPR). The best model achieved an AUROC ≥0.87 for every held-out participant and an AUPR 4-20 times the incidence rate of LOB events. Such models could be used to filter large datasets prior to manual classification by a trained healthcare provider. In this context, the models filtered out at least 65.7% of the data, while detecting ≥87.0% of events on average. Based on the demonstrated discriminative ability to separate LOBs and normal walking segments, such models could be applied retrospectively to track the occurrence of LOBs over an extended period of time. [ABSTRACT FROM AUTHOR]

Dehydration beyond 2% bodyweight loss should be monitored to reduce the risk of heat-related injuries during exercise. However, assessments of hydration in athletic settings can be limited in their accuracy and accessibility. In this study, we sought to develop a data-driven noninvasive approach to measure hydration status, leveraging wearable sensors and normal orthostatic movements. Twenty participants (10 males, 25.0 ± 6.6 years; 10 females, 27.8 ± 4.3 years) completed two exercise sessions in a heated environment: one session was completed without fluid replacement. Before and after exercise, participants performed 12 postural movements that varied in length (up to 2 min). Logistic regression models were trained to estimate dehydration status given their heart rate responses to these postural movements. The area under the receiver operating characteristic curve (AUROC) was used to parameterize the model's discriminative ability. Models achieved an AUROC of 0.79 (IQR: 0.75, 0.91) when discriminating 2% bodyweight loss. The AUROC for the longer supine-to-stand postural movements and shorter toe-touches were similar (0.89, IQR: 0.89, 1.00). Shorter orthostatic tests achieved similar accuracy to clinical tests. The findings suggest that data from wearable sensors can be used to accurately estimate mild dehydration in athletes. In practice, this method may provide an additional measurement for early intervention of severe dehydration. [ABSTRACT FROM AUTHOR]