Assisted Grasping in Individuals with Tetraplegia: Improving Control through Residual Muscle Contraction and Movement

Abstract

Individuals who sustained a spinal cord injury often lose important motor skills, and cannot perform basic daily living activities. Several assistive technologies, including robotic assistance and functional electrical stimulation, have been developed to restore lost functions. However, designing reliable interfaces to control assistive devices for individuals with C4–C8 complete tetraplegia remains challenging. Although with limited grasping ability, they can often control upper arm movements via residual muscle contraction. In this article, we explore the feasibility of drawing upon these residual functions to pilot two devices, a robotic hand and an electrical stimulator. We studied two modalities, supra-lesional electromyography (EMG), and upper arm inertial sensors (IMU). We interpreted the muscle activity or arm movements of subjects with tetraplegia attempting to control the opening/closing of a robotic hand, and the extension/flexion of their own contralateral hand muscles activated by electrical stimulation. Two groups were recruited: eight subjects issued EMG-based commands; nine other subjects issued IMU-based commands. For each participant, we selected at least two muscles or gestures detectable by our algorithms. Despite little training, all participants could control the robot’s gestures or electrical stimulation of their own arm via muscle contraction or limb motion.

Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation

“…The collected data on specific usability attributes for a wide range of IDS and their respective applications to various contexts of use can serve as a catalog of solutions for technology developers needing to select an appropriate IDS for their application. Although this work's scope is focused on ULO, its conclusions may also apply for other devices sharing similar challenges, such as neuroprosthetics (Taylor et al, 2002 ; Ambrosini et al, 2014b ; Fonseca et al, 2019 ), supernumerary limbs (Hussain et al, 2017 ; Cunningham et al, 2018 ), or prosthetics (Micera et al, 2010 ; Parajuli et al, 2019 ). The importance of a user- and application-specific selection of IDS for such devices is underlined by the finding that no IDS can be rated as being generally superior to another without specifying the detailed context of use, i.e., the type of device for which the IDS is intended, the target user's capabilities and preferences, as well as the targeted usage environment and the tasks for which the device should be used.…”

Tracking and Classification of Head Movement for Augmentative and Alternative Communication Systems

“…Lastly, while a writing task was used in this work to evaluate the feasibility of the proposed HCI system, in our research group we are interested in evaluating similar methods in other AT scenarios. Some examples applications involve human-robot interaction [16,17] and wearable systems [18].…”

Can Prosthetic Hands Mimic a Healthy Human Hand?

“…There have been several methods for controlling noninvasive prostheses, primarily by EMG or EEG signals. Additionally, in recent years, researchers have made significant attempts to utilize new technologies and to propose a new technique such as augmented reality (AR) glasses [51], inductive tongue control system (ITCS) [52], mechanomyography (MMG), voice commands and inertial measurement units (IMUs) to control prosthetic hands [49,53]. Some ideas, however, have demonstrated that even simplistic methods can deliver persuasive outcomes.…”