Biological and biomimetic machine learning for automatic classification of human gait

Machine learning (ML) research has benefited from a deep understanding of biological mechanisms that have evolved to perform comparable tasks. Recent successes of ML models, superseding human performance in human perception based tasks has garnered interest in improving them further. However, the approach to improving ML models tends to be unstructured, particularly for the models that aim to mimic biology. This thesis proposes and applies a bidirectional learning paradigm to streamline the process of improving ML models’ performance in classification of a task, which humans are already adept at. The approach is validated taking human gait classification as the exemplar task. This paradigm possesses the additional benefit of investigating underlying mechanisms in human perception (HP) using the ML models. Assessment of several biomimetic (BM) and non-biomimetic (NBM) machine learning models on an intrinsic feature of gait, namely the gender of the walker, establishes a functional overlap in the perception of gait between HP and BM, selecting the Long-Short-Term-Memory (LSTM) architecture as the BM of choice for this study, when compared with other models such as support vector machines, decision trees and multi-layer perceptron models. Psychophysics and computational experiments are conducted to understand the overlap between human and machine models. The BM and HP derived from psychophysics experiments, share qualitatively similar profiles of gender classification accuracy across varying stimulus exposure durations. They also share the preference for motion-based cues over structural cues (BM=H>NBM). Further evaluation reveals a human-like expression of the inversion effect, a well-studied cognitive bias in HP that reduces the gender classification accuracy to 37% (p<0.05, chance at 50%) when exposed to inverted stimulus. Its expression in the BM supports the argument for learned rather than hard-wired mechanisms in HP. Particularly given the emergence of the effect in every BM, after training multiple randomly initialised BM models without prior anthropomorphic expectations of gait. The above aspects of HP, namely the preference for motion cues over structural cues and the lack of prior anthropomorphic expectations, were selected to improve BM performance. Representing gait explicitly as motion-based cues of a non-anthropomorphic, gender-neutral skeleton not only mitigates the inversion effect in BM, but also improves significantly the classification accuracy. In the case of gender classification of upright stimuli, mean accuracy improved by 6%, from 76% to 82% (F1,18 = 16, p<0.05). For inverted stimuli, mean accuracy improved by 45%, from 37% to 82% (F1,18 = 20, p<0.05). The model was further tested on a more challenging, extrinsic feature task; the classification of the emotional state of a walker. Emotions were visually induced in subjects through exposure to emotive or neutral images from the International Affective Picture System (IAPS) database. The classification accuracy of the BM was significantly above chance at 43% accuracy (p<0.05, chance at 33.3%). However, application of the proposed paradigm in further binary emotive state classification experiments, improved mean accuracy further by 23%, from 43% to 65% (F1,18 = 7.4, p<0.05) for the positive vs. neutral task. Results validate the proposed paradigm of concurrent bidirectional investigation of HP and BM for the classification of human gait, suggesting future applications for automating perceptual tasks for which the human brain and body has evolved

Gender Perception From Gait: A Comparison Between Biological, Biomimetic and Non-biomimetic Learning Paradigms

This paper explores in parallel the underlying mechanisms in human perception of biological motion and the best approaches for automatic classification of gait. The experiments tested three different learning paradigms, namely, biological, biomimetic, and non-biomimetic models for gender identification from human gait. Psychophysical experiments with twenty-one observers were conducted along with computational experiments without applying any gender specific modifications to the models or the stimuli. Results demonstrate the utilization of a generic memory based learning system in humans for gait perception, thus reducing ambiguity between two opposing learning systems proposed for biological motion perception. Results also support the biomimetic nature of memory based artificial neural networks (ANN) in their ability to emulate biological neural networks, as opposed to non-biomimetic models. In addition, the comparison between biological and computational learning approaches establishes a memory based biomimetic model as the best candidate for a generic artificial gait classifier (83% accuracy, p < 0.001), compared to human observers (66%, p < 0.005) or non-biomimetic models (83%, p < 0.001) while adhering to human-like sensitivity to gender identification, promising potential for application of the model in any given non-gender based gait perception objective with superhuman performance

Neural and Neuromimetic Perception: A Comparative Study of Gender Classification from Human Gait

Humans are adept at perceiving biological motion for purposes such as the discrimination of gender. Observers classify the gender of a walker at significantly above chance levels from a point-light distribution of joint trajectories. However, performance drops to chance level or below for vertically inverted stimuli, a phenomenon known as the inversion effect. This lack of robustness may reflect either a generic learning mechanism that has been exposed to insufficient instances of inverted stimuli or the activation of specialized mechanisms that are pre-tuned to upright stimuli. To address this issue, the authors compare the psychophysical performance of humans with the computational performance of neuromimetic machine-learning models in the classification of gender from gait by using the same biological motion stimulus set. Experimental results demonstrate significant similarities, which include those in the predominance of kinematic motion cues over structural cues in classification accuracy. Second, learning is expressed in the presence of the inversion effect in the models as in humans, suggesting that humans may use generic learning systems in the perception of biological motion in this task. Finally, modifications are applied to the model based on human perception, which mitigates the inversion effect and improves performance accuracy. The study proposes a paradigm for the investigation of human gender perception from gait and makes use of perceptual characteristics to develop a robust artificial gait classifier for potential applications such as clinical movement analysis

Virtual Environment Rehabilitation for Patients with Motor Neglect Trial (VERMONT): A Single-Center Randomized Controlled Feasibility Trial.

BACKGROUND: Motor neglect occurs in patients with chronic pain conditions. Virtual environments (VE) help rehabilitation through biofeedback and improving motivation. AIM: To assess the feasibility of a VE for patients with motor neglect with chronic pain. METHODS: 10 subjects with chronic pain (Fibromyalgia, Sciatica, and Complex Regional Pain Syndrome) underwent a treadmill task three times per week for two weeks. Groups were randomized to receive real-time biofeedback from the VE (intervention) or shown still images (control). Primary outcomes were: (i) distance walked at baseline compared to the final 5 min cycle of week 2; (ii) the Lower Extremity Functional Index (LEFI) questionnaire. A satisfaction questionnaire was used. Follow up was to 24 weeks. RESULTS: Total distance walked was significantly higher in the intervention group (p < 0.05), and 33% (2/6) of the intervention group had a clinically important LEFI improvement compared to 0/4 in the control group at week 2. No secondary outcome measures demonstrated any significant differences. The intervention received high satisfaction scores, significantly greater than the control group at week 24. No harms were recorded. DISCUSSION: This feasibility study showed that VE and treadmill-walking improved walking distances and function for subjects with motor neglect. This is a promising novel approach and requires further validation through larger study