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Gait is the method of human locomotion using limbs. Recently, the analysis of human motion, specifically human gait, has received a large amount of research attention. Human gait can contain a wide variety of information that can be used in biometrics, disease diagnosis, injury rehabilitation, and load determination. In this dissertation, the development of a model-based gait analysis technique to classify external loads is presented. Specifically, the effects of external loads on gait are quantified and these effects are then used to classify whether an individual gait pattern is the result of carrying an external load or not. First of all, the reliability of using continuous relative phase as a metric to determine loading condition is quantified by intra-class correlation coefficients (ICC) and the number of required trials is computed. The ICC(2, 1) values showed moderate reliability and 3 trials are sufficient to determine lower body kinematics under two external load conditions. Then, the work was conducted to provide the baseline separability of load carriage conditions into loaded and unloaded categories using several lower body kinematic parameters. Satisfactory classification of subjects into the correct loading condition was achieved by resorting to linear discriminant analysis (LDA). The baseline performance from 4 subjects who were not included in training data sets shows that the use of LDA provides an 88.9% correct classification over two loaded and unloaded walking conditions. Extra weights, however, can be in the form of an external load carried by an individual or excessive body weight carried by an overweight individual. The study now attempts to define the differences in lower body gait patterns caused by either external load carriage, excessive body weight, or a combination of both. It was found significant gait differences due to external load carriage and excessive body weight. Principal Component Analysis (PCA) was also used to analyze the lower body gait patterns for four loading conditions: normal weight unloaded, normal weight loaded, overweight unloaded and overweight loaded. PCA has been shown to be a powerful tool for analyzing complex gait data. In this analysis, it is shown that in order to quantify the effects of external loads for both normal weight and overweight subjects, only two principal components (PCs) are needed. The results in this dissertation suggest that there are gait pattern changes due to external loads, and LDA could be applied successfully to classify the gait patterns with an unknown load condition. Both load carriage and excessive body weight affect lower body kinematics, but it is proved that they are not the same loading conditions. Methods in the current work also give a potential for new medical and clinical ways of investigating gait effects in osteoarthritis patients and/or obese people. / Ph. D.

Electrical components constantly being scaled down in size allows for small, inexpensive sensors to be placed on or around the human body for motion sensing applications. In addition, the merging of textiles with electrical components, known as electronic textiles (e-textiles), allows for these sensors to be placed directly on a wearable fabric. Simulation allows for extensive application testing and verification before prototype development. This thesis presents a simulation environment for motion sensing E-textiles. Specifically, this environment incorporates motion capture position data to simulate a rate sensing gyroscope and a dual-axis accelerometer. In addition, this simulation environment is applied to the field of gait analysis, which is the process of quantification and interpretation of a person's stride, to calculate a subject's step length. / Master of Science

The union of electronics and textiles to form electronic textiles (e-textiles) provides a promising substrate upon which motion analysis applications can be developed and implemented. Familiarity with clothing allows sensors and computational elements to be naturally integrated into garments such that wearability and usability is preserved. The dynamics of the human body and the wide variety of sensor and processing choices render the typical prototype-based design methodology prohibitively difficult and expensive. Simulation of e-textile systems not only reduces these problems but allows for thorough exploration of the design space, faster design cycles, and more robust applications. Gait analysis, the measurement of various body motion parameters during walking for medical purposes, and context awareness, the recognition of user motions, are two immediate applications that e-textiles can impact and emphasize the feasibility of e-textiles as a medium for sensor deployment on the human body. This thesis presents the design of a simulation environment for wearable e-textile systems and demonstrates the use of the simulation via a prototype pair of e-textile pants. / Master of Science

Analyzing a person's gait is important in determining their physical and neurological health. However, typical motion analysis laboratories are only in urban specialty care facilities and can be expensive due to the specialized personnel and technology needed for these examinations. Many patients, especially those who reside in underdeveloped or isolated locations, find it impractical to go to such facilities. With the help of recent developments in high-performance computing and artificial intelligence models, it is now feasible to evaluate human movement using digital video. Over the past 20 years, various visual gait analysis tools and scales have been developed. A study of the literature and discussions with physicians who are domain experts revealed that the Edinburgh Visual Gait Score (EVGS) is one of the most effective scales currently available. Clinical implementations of EVGS currently rely on human scoring of videos. In this thesis, an algorithmic implementation of EVGS scoring based on hand-held smart phone video was implemented. Walking gait was recorded using a handheld smartphone at 60Hz as participants walked along a hallway. Body keypoints representing joints and limb segments were then identified using the OpenPose - Body 25 pose estimation model. A new algorithm was developed to identify foot events and strides from the keypoints and determine EVGS parameters at relevant strides. The stride identification results were compared with ground truth foot events that were manually labeled through direct observation, and the EVGS results were compared with evaluations by human scorers. Stride detection was accurate within 2 to 5 frames. The level of agreement between the scorers and the algorithmic EVGS score was strong for 14 of 17 parameters. The algorithm EVGS results were highly correlated to scorers' scores (r>0.80) for eight of the 17 factors. Smartphone-based remote motion analysis with automated implementation of the EVGS may be employed in a patient's neighborhood, eliminating the need to travel. These results demonstrated the viability of automated EVGS for remote human motion analysis.

ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada / Even after rehabilitation, patients with lower-limb amputation may continue to exhibit suboptimal gait. A wireless telemetry system, featuring force sensors, accelerometers, control electronics and a Bluetooth transmission module was developed to measure plantar pressure information and remotely monitor patient mobility. Plantar pressure characterization studies were performed to determine the optimal sensor placement. Finally, the wireless telemetry system was integrated with a previously developed haptic feedback system in order to allow remote monitoring of patient mobility during haptic system validation trials.

Three-dimensional kinematic analysis was used to precisely quantify alterations in gait and compensatory behaviors in rat performance on beamwalk and treadmill tasks following moderate traumatic brain injury. Measures included limb height, joint angles, adduction, flexion, and swing/stance phase duration. Injury-associated changes on the treadmill included postural and hip angle change, and increases in hip height and adduction. The beamwalk presented as a more sensitive measure when coupled with kinematic analysis, as differences between injury groups were evident on measures including knee, ankle, elbow, and mid hip height. Animal response was diverse, possibly reflecting individual compensatory strategies which varied among injured animals. Kinematic analysis was ultimately shown to be a useful tool in characterizing and dissociating initial impairment, compensation, and recovery.

This paper presents a method for pedestrian activity classification and gait analysis based on the microelectromechanical-systems inertial measurement unit (IMU). The work targets two groups of applications, including the following: 1) human activity classification and 2) joint human activity and gait-phase classification. In the latter case, the gait phase is defined as a substate of a specific gait cycle, i.e., the states of the body between the stance and swing phases. We model the pedestrian motion with a continuous hidden Markov model (HMM) in which the output density functions are assumed to be Gaussian mixture models. For the joint activity and gait-phase classification, motivated by the cyclical nature of the IMU measurements, each individual activity is modeled by a "circular HMM." For both the proposed classification methods, proper feature vectors are extracted from the IMU measurements. In this paper, we report the results of conducted experiments where the IMU was mounted on the humans' chests. This permits the potential application of the current study in camera-aided inertial navigation for positioning and personal assistance for future research works. Five classes of activity, including walking, running, going upstairs, going downstairs, and standing, are considered in the experiments. The performance of the proposed methods is illustrated in various ways, and as an objective measure, the confusion matrix is computed and reported. The achieved relative figure of merits using the collected data validates the reliability of the proposed methods for the desired applications. / <p>QC 20130114</p>

Stair-climbing is an important skill for promoting independence and activities of daily life and is a key component of rehabilitation therapies for physically disabled children. This thesis describes the design and evaluation of an interactive musical stairs system for children engaged in stair-climbing physical therapies. The achievement of a targeted therapeutic goal, namely, use of reciprocal steps, was significantly increased by 6% [SD=7%] (p=0.007) with the presence of audio feedback. Levels of participant enjoyment and motivation increased as well. This led to the development of an automated system, using inertial sensors to detect initial contact (IC) events each time a child makes a step, to trigger audio feedback. A semi-generic algorithm was designed that was able to detect 96% [SD=3%] of IC events during stair-climbing therapy sessions. This thesis lays the groundwork for future longitudinal research investigating the efficacy of audio feedback in stair-climbing and other rehabilitation therapies as well.

Stair-climbing is an important skill for promoting independence and activities of daily life and is a key component of rehabilitation therapies for physically disabled children. This thesis describes the design and evaluation of an interactive musical stairs system for children engaged in stair-climbing physical therapies. The achievement of a targeted therapeutic goal, namely, use of reciprocal steps, was significantly increased by 6% [SD=7%] (p=0.007) with the presence of audio feedback. Levels of participant enjoyment and motivation increased as well. This led to the development of an automated system, using inertial sensors to detect initial contact (IC) events each time a child makes a step, to trigger audio feedback. A semi-generic algorithm was designed that was able to detect 96% [SD=3%] of IC events during stair-climbing therapy sessions. This thesis lays the groundwork for future longitudinal research investigating the efficacy of audio feedback in stair-climbing and other rehabilitation therapies as well.