Citations

Repeated durations of dynamic activity with high ground reaction forces (GRFs) and loading rates (LRs) can be beneficial to bone health. To fully characterize dynamic activity in relation to bone health, field-based measurements of gait kinetics are desirable to assess free-living lower-extremity loading. The study aims were to determine correlations of peak vertical GRF and peak vertical LR with ankle peak vertical accelerations, and of peak resultant GRF and peak resultant LR with ankle peak resultant accelerations, and to compare them to correlations with tibia, thigh, and waist accelerations. GRF data were collected as ten healthy subjects (26 [19–34] years) performed 8–10 walking trials at velocities ranging from 0.19 to 3.05 m/s while wearing ankle, tibia, thigh, and waist accelerometers. While peak vertical accelerations of all locations were positively correlated with peak vertical GRF and LR (r2 > .53, P < .001), ankle peak vertical accelerations were the most correlated (r2 > .75, P < .001). All peak resultant accelerations were positively correlated with peak resultant GRF and LR (r2 > .57, P < .001), with waist peak resultant acceleration being the most correlated (r2 > .70, P < .001). The results suggest that ankle or waist accelerometers give the most accurate peak GRF and LR estimates and could be useful tools in relating physical activity to bone health.

This study aimed to define accelerations measured at the waist and lower extremities over a range of gait velocities to provide reference data for choosing the appropriate accelerometer for field-based human activity monitoring studies. Accelerations were measured with a custom activity monitor (± 16g) at the waist, thighs, and ankles in 11 participants over a range of gait velocities from slow walking to running speeds. The cumulative frequencies and peak accelerations were determined. Cumulative acceleration amplitudes for the waist, thighs, and ankles during gait velocities up to 4.8 m/s were within the standard commercial g-range (± 6g) in 99.8%, 99.0%, and 96.5% of the data, respectively. Conversely, peak acceleration amplitudes exceeding the limits of many commercially available activity monitors were observed at the waist, thighs, and ankles, with the highest peaks at the ankles, as expected. At the thighs, and more so at the ankles, nearly 50% of the peak accelerations would not be detected when the gait velocity exceeds a walking velocity. Activity monitor choice is application specific, and investigators should be aware that when measuring high-intensity gait velocity activities with commercial units that impose a ceiling at ± 6g, peak accelerations may not be measured.

Recording body accelerations has been investigated previously to monitor health, metabolic energy expenditure, postural sway and falls [1]. Activity monitoring and the subsequent analysis of the accelerometry data have also proven useful in detecting physical activity levels among Parkinson’s [2] and osteoarthritis patients [3].

Gait analysis is an important tool in assessing the health and activity levels of patients and regular physical activity has been associated with health improvements in a number of populations. Step counting is one of the most commonly used measures of physical activity [1] and many studies have investigated the use of wearable sensors for step counts [2–4]. Their small size and light weight mean that they may be used in a free living environment and are suitable for home deployment. One of the main issues associated with step counts as a measure of physical activity is that a very high level of accuracy in step detection is needed.

A three-dimensional (3D) geometric model for predicting muscle forces in the shoulder complex is proposed. The model was applied throughout the range of arm elevation in the scapular plan. In vitro testing has been performed on 13 cadaveric shoulders. The objectives were to determine homogeneous values of physiological parameters of shoulder muscles and to locate sites of muscular attachment to any bone of the shoulder complex. Muscular fiber lengths, lengths of contractile element (CE), and muscle volumes were measured, corresponding physiological cross-sectional area (PCSA) were calculated, and force/length muscle relations were found. An in vivo biplanar radiography was performed on five volunteers. The photogrammetric reconstruction of bone axes and landmarks were coupled with a geometric modeling of bones and muscle sites of attachment. Muscular paths were drawn and changes in lengths during movement have been estimated. Directions of muscle forces are the same as that of muscular path at the point of attachment to bone. Magnitudes of muscular forces were found from muscle lengths coupled with force/length relations. Passive forces were directly determined contrary to active muscle forces. A resulting active muscle force is calculated from balancing weight and passive forces at each articular center. Active muscle forces were calculated by distributing the resulting force among active muscles based on the muscular PCSA values.