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Body-worn kinematic sensors have been widely proposed as the optimal solution for portable, low cost, ambulatory monitoring of gait. This study aims to evaluate an adaptive gyroscope-based algorithm for automated temporal gait analysis using body-worn wireless gyroscopes. Gyroscope data from nine healthy adult subjects performing four walks at four different speeds were then compared against data acquired simultaneously using two force plates and an optical motion capture system. Data from a poliomyelitis patient, exhibiting pathological gait walking with and without the aid of a crutch, were also compared to the force plate. Results show that the mean true error between the adaptive gyroscope algorithm and force plate was -4.5 ± 14.4 ms and 43.4 ± 6.0 ms for IC and TC points, respectively, in healthy subjects. Similarly, the mean true error when data from the polio patient were compared against the force plate was -75.61 ± 27.53 ms and 99.20 ± 46.00 ms for IC and TC points, respectively. A comparison of the present algorithm against temporal gait parameters derived from an optical motion analysis system showed good agreement for nine healthy subjects at four speeds. These results show that the algorithm reported here could constitute the basis of a robust, portable, low-cost system for ambulatory monitoring of gait.

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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.

Development of a flexible wireless sensor platform for measurement of biomechanical and physiological variables related to functional movement would be a vital step towards effective ambulatory monitoring and early detection of risk factors in the ageing population. The small form factor, wirelessly enabled SHIMMER platform has been developed towards this end. This study is focused assessing the utility of the SHIMMER for use in ambulatory human gait analysis. Temporal gait parameters derived from a tri-axial gyroscope contained in the SHIMMER are compared against those acquired simultaneously using the CODA motion analysis system. Results from a healthy adult male subject show excellent agreement (ICC(2, k) > 0.85) in stride, swing and stance time for 10 walking trials and 4 run trials. The mean differences using the Bland and Altman method for stance, stride and swing times were 0.0087, 0.0044 and -0.0061 seconds respectively. These results suggest that the SHIMMER is a versatile cost effective tool for use in temporal gait analysis.

Research has shown that childhood physical activity participation has a positive relationship with markers of wellbeing, such as selfesteem and quality of life, and physical activity participation may serve as protective mechanism against some mental illnesses including depression. The aim of the current study was to examine the relationship between gender, physical activity, screen time, body mass index and wellbeing in Irish school children (N = 705; mean age: 8.74 ± 0.52 years) from social disadvantage. In Northern Ireland, schools included in the 2010 Multiple Deprivation Measure (NIMDM) were invited to participate. Schools included for participation in the Republic of Ireland were from the Delivering Equality of Opportunity in Schools (DEIS) index. Data gathered included accelerometry (physical activity), self-report (screen time and wellbeing), and anthropometric measurements. Physical activity was objectively measured during eight consecutive days using Actigraph GT1M and GT3X devices, using stringent accelerometer protocol. Screen time activities were derived using questions adapted from the Health Promotion Agencies National Children's Survey in Northern Ireland. The KIDSCREEN-27 is a health-related quality of life measurement, and this tool was used by participants to selfreport their health and wellbeing. Results suggest that boys accumulated more minutes of daily screen time than girls, however, boys were more physically active when compared to girls. Wellbeing scores for gender showed inverse associations with daily screen time. Standard multiple regression revealed that gender, physical activity, screen time and body mass index (combined) explained little variance in the prediction of wellbeing. Results indicate the importance of gender-based considerations for physical activity and screen time with children

Falls in the elderly population are a major problem for today's society. The immediate automatic detection of such events would help reduce the associated consequences of falls. This paper describes the development of an accurate, accelerometer-based fall detection system to distinguish between Activities of Daily Living (ADL) and falls. It has previously been shown that falls can be distinguished from normal ADL through vertical velocity thresholding using an optical motion capture system. In this study however accurate vertical velocity profiles of the trunk were generated by simple signal processing of the signals from a tri-axial accelerometer (TA). By recording simulated falls onto crash mats and ADL performed by 5 young healthy subjects, using both a single chest mounted TA and using an optical motion capture system, the accuracy of the vertical velocity profiles was assessed. Data analysis was performed using MATLAB to determine the peak velocities recorded and RMS error during four different fall and six ADL types. Results show high correlations and low percentage errors between the vertical velocity profiles generated by the TA to those recorded using the optical motion capture system. In addition, through thresholding of the vertical velocity profiles generated using the TA at -1.3m/s, falls can be distinguished from normal ADL with 100% sensitivity and specificity.