Acta kinesiologiae Universitatis Tartuensis. 7(Supplement)

http://www.ester.ee/record=b1227224*es

Erfassung plantarer Lastverhältnisse auf kontinuierlich passiven Bewegungsschienen (CPM) und aktiven Bewegungsschienen des Kniegelenks

Erfassung plantarer Lastverhältnisse auf kontinuierlich passiven Bewegungsschienen (CPM) und aktiven Bewegungsschienen des KniegelenksAssessing Foot Loads in Continuous Passive Motion (CPM) and Active Knee Joint Motion Device

Gait Analysis Using Wearable Sensors

Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications

THE EFFECTS OF PROLONGED RUNNING ON THE BIOMECHANICS AND FUNCTION OF THE FOOT AND ANKLE

Running injuries have been linked to morphology and lower limb function, and changes in foot and ankle biomechanics and function within a run may contribute to the predisposition to injury. This thesis investigates the effects of prolonged running on the foot and ankle, and potential mechanisms underlying changes in foot posture. Methods: A series of studies were undertaken from field to laboratory, measuring foot posture changes after prolonged running of different durations. Further measures of ankle invertor strength and medial ankle stiffness were taken in the laboratory studies as well as kinematic and plantar pressure data captured every ten minutes to enable repeated measures analysis of pedal movement to be conducted. Reliability across the foot posture, strength and stiffness measures was also determined. The latter studies involved the development and mechanical testing of a novel foot orthosis component which was compared to a standard open cell orthotic foam. A double blind randomised controlled trial then compared how the novel and standard foam components affected foot posture, ankle invertor strength and medial and plantar soft tissue stiffness after a 30-minute run. Results: A mean drop in NH and increase in FPI-6 following the half marathon, hour long and 30-minute treadmill runs was seen, with changes decreasing as running duration reduced. Ankle invertor strength and medial ankle stiffness reduced but did not correlate to the change in foot posture. Changes in foot and ankle kinematics were seen within 30 minutes of running. Mechanical testing of the novel orthotic component and standard foam revealed characteristic differences in response to loading, and changes in foot posture measures after 30 minutes of running in the randomised controlled trial were almost identical across both conditions. Further comparison of invertor strength and medial foot and ankle stiffness revealed no significant differences, but a large difference between exertion measures was seen. Conclusion: There was an overall effect of duration of running on changes in foot posture in this thesis, and the foot posture change was moderated by two different foot orthosis conditions although the mechanism remains unclear.University of Plymout

Using plantar pressure for free-living posture recognition and sedentary behaviour monitoring

Health authorities in numerous countries and even the World Health Organization (WHO) are concerned with low levels of physical activity and increasing sedentary behaviour amongst the general population. In fact, emerging evidences identify sedentary behaviour as a ubiquitous characteristic of contemporary lifestyles. This has major implications for the general health of people worldwide particularly for the prevalence of non-communicable conditions (NCDs) such as cardiovascular disease, diabetes and cancer and their risk factors such as raised blood pressure, raised blood sugar and overweight. Moreover, sedentary time appears to be uniquely associated with health risks independent of physical activity intensity levels. However, habitual sedentary behaviour may prove complex to be accurately measured as it occurs across different domains, including work, transport, domestic duties and even lei¬sure. Since sedentary behaviour is mostly reflect as too much sitting, one of the main concerns is being able to distinguish among different activities, such as sitting and standing. Widely used devices such as accelerometer-based activity monitors have a limited ability to detect sedentary activities accurately. Thus, there is a need of a viable large-scale method to efficiently monitor sedentary behaviour. This thesis proposes and demonstrates how a plantar pressure based wearable device and machine learning classification techniques have significant capability to monitor daily life sedentary behaviour. Firstly, an in-depth review of research and market ready plantar pressure and force technologies is performed to assess their measurement capabilities and limitations to measure sedentary behaviour. Afterwards, a novel methodology for measuring daily life sedentary behaviour using plantar pressure data and a machine learning predictive model is developed. The proposed model and its algorithm are constructed using a dataset of 20 participants collected at both laboratory-based and free-living conditions. Sitting and standing variations are included in the analysis as well as the addition of a potential novel activities, such as leaning. Video footage is continuously collected using of a wearable camera as an equivalent of direct observation to allow the labelling of the training data for the machine learning model. The optimal parameters of the model such as feature set, epoch length, type of classifier is determined by experimenting with multiple iterations. Different number and location of plantar pressure sensors are explored to determine the optimal trade-off between low computational cost and accurate performance. The model s performance is calculated using both subject dependent and subject independent validation by performing 10-fold stratified cross-validation and leave-one-user-out validation respectively. Furthermore, the proposed model activity performance for daily life monitoring is validated against the current criterion (i.e. direct observation) and against the de facto standard, the activPAL. The results show that the proposed machine learning classification model exhibits excel-lent recall rates of 98.83% with subject dependent training and 95.93% with independent training. This work sets the groundwork for developing a future plantar pressure wearable device for daily life sedentary behaviour monitoring in free-living conditions that uses the proposed ma-chine leaning classification model. Moreover, this research also considers important design characteristics of wearable devices such as low computational cost and improved performance, addressing the current gap in the physical activity and sedentary behaviour wearable market

Parkinson's disease in the elderly patient: an objective assessment of night-time movement and daytime bradykinesia

The disability caused by Parkinson's disease in old age is difficult to quantify and the response to medication may be small. Consequently, emphasis has been placed on objective methods of assessment of night-time and daytime mobility in the following work. The size and frequency of movement during sleep were recorded using a bed movement monitor. Mean move size was reduced in those with either Parkinson's disease or a low cognitive function score. When both these factors coexisted the resultant reduction in mobility was associated with a higher prevalence of pressure sores. Furthermore, frequency of movement was inversely proportional to cognitive function score. A double-blind, placebo-controlled, cross-over study of nocturnal dosing with Sinemet-Plus in patients with Parkinson's disease was carried out. Following active treatment, there was an improvement in sleep both as assessed subjectively and by measurement of night-time mobility. Despite the long interval between dosing and morning assessment, walking time was also faster. In a further double-blind, placebo-controlled, cross-over study, a pedobarograph and a gait assessment trolley were used in a novel way to measure the effect of Sinemet-Plus on daytime bradykinesia. After active treatment, the nature of foot strike improved and the double support time was reduced. Serial plasma concentrations of levodopa and 3-0-methyldopa were measured using high-performance liquid chromatography. As duration of levodopa therapy increased there was a decrease in the area under the levodopa plasma concentration/time curve and an increase in the mean 3-0-methyldopa plasma concentration. However, no relationship was found between parameters of gait and plasma concentrations of levodopa and 3-0 methyldopa, or measurements of mean arterial blood pressure

Biomedical Sensing and Imaging

This book mainly deals with recent advances in biomedical sensing and imaging. More recently, wearable/smart biosensors and devices, which facilitate diagnostics in a non-clinical setting, have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field. The aim of this book is to provide a research forum in biomedical sensing and imaging and extend the scientific frontier of this very important and significant biomedical endeavor