For the duration of this project, I have focused on advancing in the development of a hybrid neuroprosthesis for persons with upper limb disabilities due to neural deficiencies. This device is intended for the execution of daily living activities in the context of rehabilitation. In particular, I have developed a user interface to control such device , in spite of users disabilities, by exploring their residual movements and muscle activity.
Diseases or traumas that cause neural disabilities such as spinal cord injuries (SCI) or stroke can result in major impacts in one’s life. Upper limb disabilities usually lead to a lack of autonomy in executing basic tasks such as hygiene and feeding, as well as an overall low quality of life and the occurrence of secondary health problems, e.g. obesity, pressure ulcers and depression.
Technologies aiming to assist movement in such circumstances exist. Notably, robotic exoskeletons and functional electrical stimulation (FES) can elicit movement even in paralyzed limbs. However, each has significant drawbacks that limit their use in rehabilitation clinics and/or at patients’ homes. Robotic devices are usually big and heavy not only because of the mechanical structure, but also the motors and sometimes batteries. They are expensive, rarely portable and difficult to operate. FES systems are affordable, simple, light and portable. Nevertheless, electrical stimulation elicits rapid fatigue, which makes intensive therapy impossible and limits function assistance.
The approach in my project is a hybrid system that explores the advantages of both technologies all while trying to avoid the drawbacks . The proposed device assists in elbow and hand function, consisting in two modules. The elbow module has a break system that does not elicit movement, but is capable of holding the present joint position. This is intended to mitigate fatigue by deactivating the FES when the break is on. The hand module is fully actuated by FES. Both modules rely on FES for movement, eliminating the need for motors. This reduces overall weight and external energy requirements. The device is intended for the execution of various tasks, including activities of daily living (ADL), in the context of rehabilitation.
Among all scientific and technical challenges around the development of such a system, my project focuses on the user interface. The person wearing the device has an upper limb disability which limits their capability of manipulating any standard controls, such as hand buttons or joysticks. Nevertheless, this population usually retains some residual movements and muscle contraction capabilities, particularly around the shoulders. This work explores these residual voluntary actions as inputs and translates them into the device’s commands according to the decoded user intent.
This project addressed the above-mentioned challenges by using wearable sensors capable of measuring movement – inertial measuring units (IMUs) – and muscle activity – electromyography (EMG). These sensors were positioned on specific body parts on the user, depending on their personal skills. Signals acquired were processed and decoded into user intended by machine learning algorithms. All that was done with a user centered paradigm, including a multi-center user requirements questionnaire. Finally, a hybrid upper limb neuroprosthesis was designed and a first prototype was built.
The project’s objective is to develop a high-level user interface with which persons with upper limb disabilities can control neuroprostheses for ADLs and rehabilitation.
Because the target user has no hand function, the interface cannot rely on traditional strategies such as hand operated buttons or joysticks. Instead, I employed IMUs and EMG sensors to explore residual movements and muscle activity. Similar solutions have been employed in other contexts with limited success. This project aimed at developing such user interface to control a semi passive hybrid neuroprosthesis for ADL in rehabilitation for persons that suffered a stroke or SCI.
