This research explores the estimation of pain intensity of physiological signals, as an approach for the sonification of the footwear influence on gait patterns. Aiming to provide knowledge on the impact of the footwear on pain assessment, detection, and awareness from an auditory feedback during walk. With the objective to contribute directions for future research efforts for affective pain recognition from gait patterns, with applications in physiotherapy, rehabilitation, and pain prediction.
We investigate the strategies that each person utilises to compensate muscular endurance and the limits of muscle control for any inadequate gait patterns; we also explore the role of embodiment in the structuring of consciousness to predict sensory feedback to facilitate self-esteem and motor behaviour. We analyse pain behaviour, and the systems developed to assist components of pain behaviour. As a method to increment sensory feedback on gait assistance from sonification, enhancing new modalities of perception and awareness of the body movements.
Our aim is to assess the biomechanics throughout the manifestation of fatigue during walk, in order to evaluate pain behaviours during an activity. To decode human-generated signals related to emotion from our feet, by developing a system capable of sensing affective data.
This first study, follows studies on gait analysis, by focusing on the metabolic cost of gait and by adding an extra layer of affective information.
PhD research student at University College London (UCL), since 2018, working analysing the influence of footwear on the audification of pain.
Affective computing, Pain intensity, physiological signals, well-being, impairment, body perception, pain assessment, pain behaviour, interactive sonification, motor learning, biofeedback, re-education of motor control, Electromyography, walk, machine learning, gait analysis, motion capture system, affective posture, cognition, smart materials, footwear, posture compensation, High heels, muscle activation, biomechanics, smart composites, human-computer interaction (HCI), affect detection, affect generation.