The elderly population is growing at a rapid pace, and falls are a significant problem facing adults aged 65 and older in terms of both human suffering and economic losses. Falls are the leading cause of mortality among older adults, and non-fatal falls result in reduced function and poor quality of life for older adults. Although much is known about the mechanisms and contributing risk factors relevant to falls, falls still remain a significant problem associated with this age group. Therefore, new strategies and knowledge need to be introduced to understand and prevent falls. Studies show that early detection of impaired mobility is critical to the prevention of falls. In this study, the relationship between gait and postural parameters and falls among elderly participants using wearable inertial sensors was investigated. As such, the aim of this study is to investigate the critical gait and postural parameters contributing to falls, then further to classify fallers and non-fallers by utilizing gait and postural parameters and machine learning techniques, e.g. support vector machines (SVMs). Additionally, as the assessment of fall risk is linked to noisy environment, it is important to understand the capability of the SVM classifier to effectively address noisy data. Therefore, the robustness of the SVM classifier was also investigated in this study. In summary, the presented work addresses several challenges through research on the following three issues: 1) the significant differences in gait and pastoral parameters between fallers and non-fallers; 2) a machine learning based framework for classification of fallers and non-fallers by using only one IMU located at the sternum; and 3) robustness of SVM classifier to classify fallers and non-fallers in a noisy environment. The machine learning based framework developed in this dissertation contribute to advancing the state-of-art in fall risk assessment by 1) classifying fallers and non-fallers from a single IMU located at the sternum; 2) developing machine learning method for classification of fallers and non-fallers; and 3) investigating the robustness of SVM classifier in a noisy environment. / Ph. D.

Angiotensin Converting Enzyme (ACE) IID, and a actinin 3 (AC1N3) R577X polymorphisms have been linked to the strength and power performance of elite athletes and suggested to influence skeletal muscle function in the general popUlation. This research investigated the association of these two candidate gene polymorphisms with the muscle function of young and older men, and the distribution of these genotypes in frequent fallers compared to controls. Muscle function measurements of young and older men included isometric strength, absolute and relative isokinetic strength at high velocity (ratio of torque at 2400 ·s"; torque at 30°·s") and the time course of an evoked twitch. Additionally body composition was measured by skinfold thickness (young men) and DXA scanning (old men) to estimate fat-free mass, an index of muscularity, and fat mass. ACE and AC1N3 genotypes were determined from whole blood samples using polymerase chain reaction, and serum ACE activity using spectrophotometry. The gemtypes of frequent fallers referred to a Falls Clinic were compared to a control group of healthy men. ACE genotype was not associated with any measure of muscle function, including the time course of an evoked twitch or absolute and relative high velocity torque, or body composition in these populations (ANOVA, 0.12<P<0.97). Serum ACE activity appeared to be weakly associated with knee extensor (R = 0.19, P = 0.07) and elbow flexor (R = 0.20, P = 0.06) isometric strength in older men, and was negatively correlated with the relative torque at high velocity (R = -0.23, P = 0.03). AC1N3 genotype was associated with fat mass in older men (P = 0.04), but was not associated with any measure of muscle function or muscularity (KruskalWaIIis, 0.26<P<0.95). Finally there was no apparent difference in the distribution of ACE IID (r: = 0.54, P = 0.77) and AC1N3 RIX (r: = 0.76, P = 0.68) genotypes between frequent fallers and controls. Any influence of these individual polymorphisms seems unlikely to be of sufficient magnitude to produce genotype related differences in muscle function in young or older free living UK Caucasian men. Serum ACE activity may have a small association with the isometric and dynamic strength of older men. However, AC1N3 genotype was associated with increased fat mass in XX individuals, that suggests this polymorphism may have an association with the accumulation of body fat over the life span of older men.

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