REARM is a self-directed sensor-based arm rehabilitation system, including a wearable sensor-based device and game guidance, aiming at helping children with cerebral palsy complete low-cost and accurate rehabilitation at home.
Among various conditions that lead to disability, Cerebral Palsy is one of the most common impairments that cause disability in children. Estimated figures indicate that 1.5 to 2.5 children per 1000 live births suffer from Cerebral Palsy (Gupta & Appleton, 2001). Cerebral Palsy is a group of motor-neuron diseases that occur pre-natal, during birth or early stages of development of infants.
According to the interview, this father’s son has LGS cerebral palsy. He needs to accompany his child to hospital for rehabilitation once a week. Most of the time he help his son do rehabilitation exercise at home. The problem is that going to hospital cost a lot. He doesn’t get any instruction or feedback of rehabilitation at home.
According to the interview, we got to know rehabilitation resource is not placed enough emphasis even in big hospital in Shanghai. The method of arm rehabilitation is to repeatedly stretch each joint of arm. Patients can rewire the brain through neuroplasticity. This is how the brain heals itself after stroke and starts to regain functions.
Aimed at a more convenient, accurate and low cost rehabilitation experience, a self-directed sensor-based arm rehabilitation system is designed. This system contains a wearable device on arm that senses the degree of recovery and control the level of resistance, as well as a gamification system that guides user to an accurate rehabilitation process.
This rehabilitation system employs simple game to guide kids how to conduct daily rehabilitation process, including finger training, elbow training and shoulder training. Together with the sensor-based wearable device, the system will feedback accurate improvement so as to encourage the kids to keep on the progress.
The first prototype employed LeapMotion to capture finger activity data. The limitation of only finger training leads to the second prototype that build soft sensor at each joint of the arm. This prototype includes Arduino Nano board, 5 soft sensors, and EMG sensors. Soft sensors detect movement of each joints. EMG sensor generates Electromyography graph.
Prototype of the wearable device
Realtime EMG data feedback on screen
Prototype of liquid pump that controls the level of resistance
The game was designed to guide the user to perform the daily rehabilitation exercise. This demo is designed to exercise the finger stretch. User is guided to stretch each finger to help each flower grow up with an objective outline.
Game demo for guiding finger rehabilitation