An instrumented version of the five-times-sit-to-stand test was performed in the homes of a group of older adults, categorised as fallers or non-fallers. Tri-axial accelerometers were secured to the sternum and anterior thigh of each participant during the assessment. Accelerometer data were then used to examine the timing of the movement, as well as the root mean squared amplitude, jerk and spectral edge frequency of the mediolateral (ML) acceleration during the total assessment, each sit-stand-sit component and each postural transition (sit-stand and stand-sit). Differences between fallers and non-fallers were examined for each parameter. Six parameters significantly discriminated between fallers and non-fallers: sit-stand time, ML acceleration for the total assessment, and the ML spectral edge frequency for the complete assessment, individual sit-stand-sit components, as well as sit-stand and stand-sit transitions. These results suggest that each of these derived parameters would provide improved discrimination of fallers from non-fallers, for the cohort examined, than the standard clinical measure - the total time to complete the assessment. These results indicate that accelerometry may enhance the utility of the five-times-sit-to-stand test when assessing falls risk.
Human beings have evolved an elaborate neurological control system to maintain cerebral perfusion during orthostatic challenge. In people with MCI, this response is impaired and renders them twice as likely to convert to dementia.
This study compares the performance of algorithms for body-worn sensors used with a spatiotemporal gait analysis platform to the GAITRite electronic walkway. The mean error in detection time (true error) for heel strike and toe-off was 33.9 ± 10.4 ms and 3.8 ± 28.7 ms, respectively. The ICC for temporal parameters step, stride, swing and stance time was found to be greater than 0.84, indicating good agreement. Similarly, for spatial gait parameters—stride length and velocity—the ICC was found to be greater than 0.88. Results show good to excellent concurrent validity in spatiotemporal gait parameters, at three different walking speeds (best agreement observed at normal walking speed). The reported algorithms for body-worn sensors are comparable to the GAITRite electronic walkway for measurement of spatiotemporal gait parameters in healthy subjects.
This paper draws attention to the need for further understanding of the fine details of routine and taken-for-granted daily activities and mobility. It argues that such understanding is critical if technologies designed to mitigate the negative impacts of falls and fear-of-falling are to provide unobtrusive support for independent living. The reported research was part of a large, multidisciplinary, multi-site research programme into responses to population ageing in Ireland, Technologies for Independent Living (TRIL). A small, exploratory, qualitative life-space diary study was conducted. Working with eight community-dwelling older adults with different experiences of falls or of fear-of-falls, data were collected through weekly life-space diaries, daily-activity logs, two-dimensional house plans and a pedometer. For some participants, self-recording of their daily activities and movements revealed routine, potentially risky behaviour about which they had been unaware, which may have implications for falls-prevention advice. The findings are presented and discussed around four key themes: ‘being pragmatic’, ‘not just a faller’, ‘heightened awareness and blind spots’ and ‘working with technology’. The findings suggest a need to think creatively about how technological and other solutions best fit with people's everyday challenges and needs and of critical importance, that their installation does not reduce an older adult to ‘just a faller’ or a person with a fear-of-falls.
The Irish Longitudinal Study on Ageing (TILDA) collected phasic blood pressure (pBP) data on over 5,000 participants in Wave 1. This required a Signal Processing Framework (SPF) for automating: 1) artefact rejection, and, 2) the extraction of clinically-useful features. The framework developed reduced the workload of the screening clinician by 43%. The work outlined in this paper details key steps in analysing a large dataset of pBP data and highlights the signal processing challenges encountered in modern epidemiological studies.
Wireless sensor networks have become increasingly common in everyday applications due to decreasing technology costs and improved product performance, robustness and extensibility. Wearable physiological monitoring systems have been utilized in a variety of studies, particularly those investigating ECG or EMG during human movement or sleep monitoring. These systems require extensive validation to ensure accurate and repeatable functionality. Here we validate the physiological signals (EMG, ECG and GSR) of the SHIMMER (Sensing Health with Intelligence, Modularity, Mobility and Experimental Reusability) against known commercial systems. Signals recorded by the SHIMMER EMG, ECG and GSR daughter-boards were found to compare well to those obtained by commercial systems.
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