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Towards personalized augmented feedback for gait: influence of modality and signal characteristics
Personalized and intensive rehabilitation has been shown to be highly beneficial for stroke patients to recover mobility. However, the increasing patients-to-therapist ratio and the raise of healthcare costs limit its feasibility. In this context, technological assistance such as wearable augmented feedback is of special interest to support patients. Feedback has been shown to facilitate modification of gait and to improve rehabilitation outcomes. Yet, the design of feedback modalities is often an imposed choice and we lack insights on how feedback can be shaped to be more impactful. This project aims at filling this gap by investigating the impact of different signal stimuli on gait training.
Augmented feedback (FB) provided during therapy can help post-stroke patients to relearn correct gait patterns. Yet, there are no guidelines for the selection of either FB modality (e.g., haptic, visual or audio) nor FB signal characteristics (e.g., continuous or sequential).
In this project, you will investigate whether gait pattern correction differ depending on FB modality and signal characteristics. It will be divided into two phases: (i) a pilot study with healthy participants, followed by (ii) a study with post-stroke individuals. All participants will perform one gait training session while being asked to modify their gait depending on the FB they receive. A usability interview with post-stroke participants is also planned, to collect their opinions on the different FB stimuli.
This study will provide the necessary insights to develop personalized FB-based gait therapy after stroke.
This project is conducted in collaboration with cereneo research center. It is part of the StimuLOOP consortium, which gathers 6 different labs of ETHZ and UZH. StimuLOOP project aims at developing new tools for post-stroke and Parkinson's therapy, through real-time feedback and targeted memory reactivation.
Augmented feedback (FB) provided during therapy can help post-stroke patients to relearn correct gait patterns. Yet, there are no guidelines for the selection of either FB modality (e.g., haptic, visual or audio) nor FB signal characteristics (e.g., continuous or sequential). In this project, you will investigate whether gait pattern correction differ depending on FB modality and signal characteristics. It will be divided into two phases: (i) a pilot study with healthy participants, followed by (ii) a study with post-stroke individuals. All participants will perform one gait training session while being asked to modify their gait depending on the FB they receive. A usability interview with post-stroke participants is also planned, to collect their opinions on the different FB stimuli. This study will provide the necessary insights to develop personalized FB-based gait therapy after stroke.
This project is conducted in collaboration with cereneo research center. It is part of the StimuLOOP consortium, which gathers 6 different labs of ETHZ and UZH. StimuLOOP project aims at developing new tools for post-stroke and Parkinson's therapy, through real-time feedback and targeted memory reactivation.
The goal of this project is to study the impact of different feedback stimuli (visual, audio and haptic) on feedback-based gait training, in order to identify the characteristics of an efficient feedback.
The goal of this project is to study the impact of different feedback stimuli (visual, audio and haptic) on feedback-based gait training, in order to identify the characteristics of an efficient feedback.
In this project, you will:
- conduct measurements with healthy participants and stroke patients with motion capture and feedback devices, on a motion platform (infrastructure in cereneo research center in Vitznau (LU)),
- perform gait analysis on the data collected,
- compare the effects of the different feedback stimuli and come up with final recommandations on feedback design for gait rehabilitation.
In this project, you will:
- conduct measurements with healthy participants and stroke patients with motion capture and feedback devices, on a motion platform (infrastructure in cereneo research center in Vitznau (LU)),
- perform gait analysis on the data collected,
- compare the effects of the different feedback stimuli and come up with final recommandations on feedback design for gait rehabilitation.
- Enrolled as a student in Mechanical Engineering, Biomechanics, Health Science and Technology, or related fields
- Knowledge of scientific computing languages (Matlab, Python)
- Natural enthusiasm for interacting with people and working with patients
- Strong conceptional skills, independent working style
- Experience with motion capture is an appreciated plus
- Enrolled as a student in Mechanical Engineering, Biomechanics, Health Science and Technology, or related fields
- Knowledge of scientific computing languages (Matlab, Python)
- Natural enthusiasm for interacting with people and working with patients
- Strong conceptional skills, independent working style
- Experience with motion capture is an appreciated plus
Mathilde Lestoille, PhD
Rehabilitation Engineering Lab ETH Zürich
mathilde.lestoille@hest.ethz.ch
Mathilde Lestoille, PhD Rehabilitation Engineering Lab ETH Zürich mathilde.lestoille@hest.ethz.ch