Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly

Abstract

A new method of physical activity monitoring is presented, which is able to detect body postures (sitting, standing, and lying) and periods of walking in elderly persons using only one kinematic sensor attached to the chest. The wavelet transform, in conjunction with a simple kinematics model, was used to detect different postural transitions (PTs) and walking periods during daily physical activity. To evaluate the system, three studies were performed. The method was first tested on 11 community-dwelling elderly subjects in a gait laboratory where an optical motion system (Vicon) was used as a reference system. In the second study, the system was tested for classifying PTs (i.e., lying-to-sitting, sitting-to-lying, and turning the body in bed) in 24 hospitalized elderly persons. Finally, in a third study monitoring was performed on nine elderly persons for 45-60 min during their daily physical activity. Moreover, the possibility-to-perform long-term monitoring over 12 h has been shown. The first study revealed a close concordance between the ambulatory and reference systems. Overall, subjects performed 349 PTs during this study. Compared with the reference system, the ambulatory system had an overall sensitivity of 99% for detection of the different PTs. Sensitivities and specificities were 93% and 82% in sit-to-stand, and 82% and 94% in stand-to-sit, respectively. In both first and second studies, the ambulatory system also showed a very high accuracy (> 99%) in identifying the 62 transfers or rolling out of bed, as well as 144 different posture changes to the back, ventral, right and left sides. Relatively high sensitivity (> 90%) was obtained for the classification of usual physical activities in the third study in comparison with visual observation. Sensitivities and specificities were, respectively, 90.2% and 93.4% in sitting, 92.2% and 92.1% in "standing + walking," and, finally, 98.4% and 99.7% in lying. Overall detection errors (as percent of range) were 3.9% for "standing + walking," 4.1% for sitting, and 0.3% for lying. Finally, overall symmetric mean average errors were 12% for "standing + walking," 8.2% for sitting, and 1.3% for lying.

Outcome evaluation in shoulder surgery using 3D kinematics sensors

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Mobility assessment in older people: new possibilities and challenges

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“…A BFS based gait analysis, showed that spatiotemporal gait parameters, such as walking speed, step length and the duration of bipedal stance, correspond well to the scores of older people with high and low fall-risks, based on the Tinetti score. However, the Tinetti score is much less discriminatory than the gait parameters for subjects with a moderate risk of falling (NajaW et al 2003a, b). Estimations of gait variability by means of an ambulatory system with a tri-axial accelerometer on the lower trunk, showed lower left-right, but higher vertical and forward trunk variability in frail elderly when compared to a Wt control group (Moe-Nilssen and Helbostad 2005).…”

Recognition of Falls and Activities of Daily Living using Tri-axial Accelerometer and Bi-axial Gyroscope

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