MIDAS: Multisensorial Immersive Dynamic Autonomous System hero
2021

MIDAS: Multisensorial Immersive Dynamic Autonomous System

Design-led Biosensing Exoskeleton with VR
Themes Wearable, Body, Biosignals, Medical, Systems
Materials
Physical Microcontroller, Sensor, VR Headset
Digital XR, Simulation, Sound
Data Biosignals
Systems
Software Unity
Hardware Microcontroller, Sensors
Fabrication 3D Printer
Overview

MIDAS (Multi-sensorial Immersive Dynamic Autonomous System) is a proof-of-concept rehabilitation system designed to improve motivation for hand rehabilitation in stroke-affected patients. It engages four of five primary senses—tactility, visual, auditory, and olfactory—through an integrated set of physical and immersive subsystems.

Developed through an interdisciplinary approach spanning rehabilitation, design/aesthetics, and wearable thinking, the project frames immersion and self-affirmation as part of the rehabilitation experience—not only mechanical assistance.

System components

The system consists of three main subsystems: (1) a hand exoskeleton, (2) a VR subsystem (headset + controller + game), and (3) an olfactory subsystem for scent delivery.

Intention is captured using EMG signals from the forearm to trigger physical assistance for opening/closing the fingers, connecting patient effort to both tactile support and in-game action.

Design principles

MIDAS is designed to be lightweight, portable, customizable, and affordable, with an emphasis on reproducibility via 3D printable parts and open-source-oriented development.

Beyond VR-as-motivation, MIDAS adds smell to deepen immersion, and introduces a wearable sleeve concept aimed at supporting proprioception via vibratory stimulation (weaved piezoelectrics).

Pilot study

A pilot study was conducted across three sessions with five stroke-affected participants, progressively adding subsystems per session. Reported results include high motivation scores (SRMS), strong excitement ratings, and no undesired side effects.

Credits & publication

Authors (paper): Fok-Chi-Seng Fok Kow, Anoop Kumar Sinha, Zhang Jin Ming, Bao Songyu, Jake Tan Jun Kang, Hong Yan Jack Jeffrey, Galina Mihaleva, Nadia Magnenat Thalmann, Yiyu Cai.