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.
Reliable real-time detection of ice formation is a critical enabling technology in improving rotorcraft safety. The development of a real-time in-flight ice detection system using computational aeroacoustics and Bayesian neural networks is presented and evaluated within this paper. A finite NACA0012 airfoil section undergoing sinusoidal pitching is used to represent the cyclic motion characteristic of a rotor in forward flight. Experimental ice shape measurements from the NASA Glenn Icing Research Wind Tunnel are used for validation of the numerical ice shapes. The flow-fields of the computed ice shapes are then determined using a hybrid RANS/LES approach and the acoustic noise signals of the ice shapes are predicted using the solid-surface Ffowcs Williams and Hawkings (FWH) analogy. A Bayesian neural network is then trained using the ice shapes and the performance indicators are mapped to the acoustic noise signals. This framework thus allows for the detection of ice and significantly the type of ice accreted over the pitching wing through differentiating between iced noise signals.
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