Assessment of postural sway with a pendant-mounted wearable sensor

Sensor-Based Gait and Balance Assessment in Healthy Adults: Analysis of Short-Term Training and Sensor Placement Effects

While the analysis of gait and balance can be an important indicator of age- or disease-related changes, it remains unclear if repeated performance of gait and balance tests in healthy adults leads to habituation effects, if short-term gait and balance training can improve gait and balance performance, and whether the placement of wearable sensors influences the measurement accuracy. Healthy adults were assessed before and after performing weekly gait and balance tests over three weeks by using a force plate, motion capturing system and smartphone. The intervention group (n = 25) additionally received a home-based gait and balance training plan. Another sample of healthy adults (n = 32) was assessed once to analyze the impact of sensor placement (lower back vs. lower abdomen) on gait and balance analysis. Both the control and intervention group exhibited improvements in gait/stance. However, the trends over time were similar for both groups, suggesting that targeted training and repeated task performance equally contributed to the improvement of the measured variables. Since no significant differences were found in sensor placement, we suggest that a smartphone used as a wearable sensor could be worn both on the lower abdomen and the lower back in gait and balance analyses.

Daily Posture Behavior Patterns Derived From Multitime-Scale Topic Models Using Wearable Triaxial Acceleration for Assessment of Concern About Falling

Concern about falling is prevalent in older population. This condition would cause a series of adverse physical and psychological consequences for older adults' health. Traditional assessment of concern about falling is relied on self-reported questionnaires and thus is too subjective. Therefore, we proposed a novel multi-time-scale topic modelling approach to quantitatively evaluate concern about falling by analyzing triaxial acceleration signals collected from a wearable pendent sensor. Different posture segments were firstly recognized to extract their corresponding feature subsets. Then, each selected feature related to concern about falling was clustered into discrete levels as feature letters of artificial words in different time scales. As a result, all older participants' signal recordings were converted to a collection of artificial documents, which can be processed by natural language processing methodologies. The topic modelling technique was used to discover daily posture behavior patterns from these documents as discriminants between older adults with different levels of concern about falling. The results indicated that there were significant differences in distributions of posture topics between groups of older adults with different levels of concern about falling. Additionally, the transitions of posture topics over daytime and nighttime revealed temporal regularities of posture behavior patterns of older adult's active and inactive status, which were substantially different for older adults with different levels of concern about falling. Finally, the level of concern about falling was accurately determined with accuracy of 71.2% based on the distributions of posture topics combined with the mobility performance metrics of walking behaviors and demographic information.

Rehabilitation Outcomes Following Hip Fracture of Home-Based Exercise Interventions Using a Wearable Device-A Randomized Controlled Pilot and Feasibility Study

Although hip fractures are common and severe, there is insufficient evidence concerning which type of rehabilitation is most beneficial. The primary aim of this three-armed pilot study was to investigate any difference in outcome after hip fractures between and within groups in terms of balance, everyday activities, and health-related quality of life (HRQoL) following different home rehabilitation interventions. Further aims were to study feasibility and to suggest, if necessary, adjustments to the protocol for a future full randomized controlled trial (RCT). In total, 32 persons were included in this study. The intervention groups underwent the HIFE program with or without an inertial measurement unit, while the control group underwent standard rehabilitation. Within- and between-groups differences in outcomes and feasibility outcomes in terms of recruitment and retention rates were analyzed, and the ability to collect primary and secondary outcomes was assessed. Balance, measured as postural sway, showed no significant improvement in any group. All three groups improved in functional balance (p = 0.011-0.028), activity of daily living (p = 0.012-0.027), and in HRQoL (p = 0.017-0.028). There were no other significant changes within or between the groups. The recruitment rate was 46%, the retention rate was 75%, and the ability to collect outcome measures was 80% at baseline and 64% at follow-up. Based on the results, it is possible to, after adjusting the protocol, conduct a full RCT.

A Comparison among Different Strategies to Detect Potential Unstable Behaviors in Postural Sway

Assistive Technology helps to assess the daily living and safety of frail people, with particular regards to the detection and prevention of falls. In this paper, a comparison is provided among different strategies to analyze postural sway, with the aim of detecting unstable postural status in standing condition as precursors of potential falls. Three approaches are considered: (i) a time-based features threshold algorithm, (ii) a time-based features Neuro-Fuzzy inference system, and (iii) a Neuro-Fuzzy inference fed by Discrete-Wavelet-Transform-based features. The analysis was performed across a wide dataset and exploited performance indexes aimed at assessing the accuracy and the reliability of predictions provided by the above-mentioned strategies. The results obtained demonstrate valuable performances of the three considered strategies in correctly distinguishing among stable and unstable postural status. However, the analysis of robustness against noisy data highlights better performance of Neuro-Fuzzy inference systems with respect to the threshold-based algorithm.