Active upper limb prostheses: a review on current state and upcoming breakthroughs

Abstract

The journey of a prosthetic user is characterized by the opportunities and the limitations of a device that should enable activities of daily living (ADL). In particular, experiencing a bionic hand as a functional (and, advantageously, embodied) limb constitutes the premise for promoting the practice in using the device, mitigating the risk of its abandonment. In order to achieve such a result, different aspects need to be considered for making the artificial limb an effective solution to accomplish activities of daily living. According to such a perspective, this review aims at presenting the current issues and at envisioning the upcoming breakthroughs in upper limb prosthetic devices. We first define the sources of input and feedback involved in the system control (at user-level and device-level), alongside the related algorithms used in signal analysis. Moreover, the paper focuses on the user-centered design challenges and strategies that guide the implementation of novel solutions in this area in terms of technology acceptance, embodiment, and, in general, human-machine integration based on co-adaptive processes. We here provide the readers (belonging to the target communities of researchers, designers, developers, clinicians, industrial stakeholders, and end-users) with an overview of the state-of-the-art and the potential innovations in bionic hands features, hopefully promoting interdisciplinary efforts for solving current issues of ULPs. The integration of different perspectives should be the premise to a transdisciplinary intertwining leading to a truly holistic comprehension and improvement of the bionic hands design. Overall, this paper aims to move the boundaries in prosthetic innovation beyond the development of a tool and towards the engineering of human-centered artificial limbs.

Merging Humans and Neuroprosthetics through Regenerative Peripheral Nerve Interfaces

“…The primary hurdle in achieving optimal patient–neuroprosthetic interface thus far is not solely hardware or software-related, but also centers on the signal quality. 6 The RPNI not only represents a clinical milestone by leveraging the inherent regenerative properties of transected peripheral nerves to reinnervate end organ targets for improved motor signal quality and sensory feedback, but it also introduces a pioneering engineering platform. This platform has the potential to incorporate cutting-edge engineering techniques to develop neuroprosthetic devices that can seamlessly integrate with humans, including ultraflexible, smart implants to interface with the muscle graft RPNIs, 61 advanced algorithms for data processing and decoding, 62 biomimetic encoding strategies that facilitate neuron-like sensory signaling, 32 57 and dexterous robotic devices that execute precise motor commands with multiple DoFs.…”

“…1). 6 State-of-the-art research in neuroprosthesis development aims to improve integration into the human body and has shown promise in recent clinical trials. [7][8][9][10] In the past decade, many research groups have developed functional neuroprosthetic interfaces that enable motor control using brain signals, such as electrocorticogram 11,12 and electroencephalogram, 13,14 as well as surface electromyography (EMG) signals.…”

Decoding of unimanual and bimanual reach-and-grasp actions from EMG and IMU signals in persons with cervical spinal cord injury

“…Previous experiments have shown that tree-based models such as Random Forests seem to perform well in a supervised multimodal HMI for activity recognition [24]. Additional factors such as a co-adaptive environment can further enhance HMI performance [30,31]. Hereby, human and machine interact through closed feedback loops in a mutual learning environment.…”

Arm muscle synergies enhance hand posture prediction in combination with forearm muscle synergies

“…In fact, muscle synergy theory has been already adapted as a framework for proportional myoelectric control [23]. In this context, synergy computation results in the dimensionality reduction of many input EMG signals, by obtaining few time-varying continuous signals used to control the actuators of a robotic prosthesis [24]. However, such an approach implies that each synergy must represent one of the two rotational directions (clockwise and anti-clockwise) of a DC electric motor actuating a single degree of freedom (DoF) [23].…”