Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units

Abstract

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.

Influence of the Antenna Orientation on WiFi-Based Fall Detection Systems

“…For this reason, it is necessary to determine the rate at which the falling event can be registered and select the sensor that best suits these conditions. However, the placement of the sensor on the human body directly affects the detection performance of the system [ 5 , 17 , 18 ]. Additionally, wearable sensors can cause discomfort for users who are not accustomed to wearing such devices.…”

“…For this reason, it is necessary to provide systems for remote healthcare and monitoring. Several systems have been developed for this purpose using well-established sensing techniques, such as acoustic sensors, video cameras, vibration sensors, and wearable devices [ 2 , 3 , 4 , 5 , 6 ]. However, an innovative technique has emerged in the last years: device-free monitoring based on radio-frequency (RF) signals.…”

Automated Detection of Older Adults’ Naturally-Occurring Compensatory Balance Reactions: Translation From Laboratory to Free-Living Conditions

“…Although CBRs happen more often during gait [22], a CBR detection model dependent on a gait detection algorithm (e.g., [15], [22]) may exhibit limited performance in some scenarios (see section IV). Thus, differentiation between CBRs from all other activities of daily living was hypothesized to be a more promising approach, and considered in this study.…”

“…Previous CBR detection studies [19]- [22] considered alternate methods of model training and performance assessment, such as k-fold and leave-one-subject-out cross-validation. Similar to our previous research works [21], [23], we hypothesize in the current study that incorporating a training dataset curated from data sources that are independently collected from the test dataset would result in the machine learning models with more realistic results in terms of generalization to unseen data (although lower accuracies are expected to be obtained compared to the cross-validation methods where training and test datasets share very similar distributions, e.g., k-fold [21]).…”

“…For instance, trips were reported to be the most common CBR type (e.g., in PD fallers [14]) and are potentially easier to be captured compared to the other CBR types during free-living data collection. While the investigation of CBRs in the sagittal plane has attracted more attention from the researchers (e.g., in [17], [22]), the ability to detect different CBR types, including those in the frontal plane, may provide a more comprehensive insight into older adults' balance impairment. This can be addressed by training models on a comprehensive dataset that includes samples from different CBR types (e.g., crossover, sidestep, slip-like).…”