A Multifunctional Adaptive and Interactive AI system to support people living with stroke, acquired brain or spinal cord injuries: A study protocol

Background: Acquired brain injury and spinal cord injury are leading causes of severe motor disabilities impacting a person's autonomy and social life. Enhancing neurological recovery driven by neurogenesis and neuronal plasticity could represent future solutions; however, at present, recovery of activities employing assistive technologies integrating artificial intelligence is worthy of examining. MAIA (Multifunctional, adaptive, and interactive AI system for Acting in multiple contexts) is a human-centered AI aiming to allow end-users to control assistive devices naturally and efficiently by using continuous bidirectional exchanges among multiple sensorimotor information. Methods: Aimed at exploring the acceptability of MAIA, semi-structured interviews (both individual interviews and focus groups) are used to prompt possible end-users (both patients and caregivers) to express their opinions about expected functionalities, outfits, and the services that MAIA should embed, once developed, to fit end-users needs. Discussion: End-user indications are expected to interest MAIA technical, health-related, and setting components. Moreover, psycho-social issues are expected to align with the technology acceptance model. In particular, they are likely to involve intrinsic motivational and extrinsic social aspects, aspects concerning the usefulness of the MAIA system, and the related ease to use. At last, we expect individual factors to impact MAIA: gender, fragility levels, psychological aspects involved in the mental representation of body image, personal endurance, and tolerance toward AT-related burden might be the aspects end-users rise in evaluating the MAIA project

Automated prompting technologies in rehabilitation and at home

Purpose - The purpose of this paper is to test the efficacy of an interactive verbal prompting technology (Guide) on supporting the morning routine. Data have already established the efficacy of such prompting during procedural tasks, but the efficacy of such prompting in tasks with procedural and motivational elements remains unexamined. Such tasks, such as getting out of bed in the morning and engaging in personal care, are often the focus of rehabilitation goals. Design/methodology/approach - A single-n study with a male (age 61) who had severe cognitive impairment and was having trouble completing the morning routine. An A-B-A'-B'-A?-B? design was used, with the intervention phase occurring both in an in-patient unit (B, B') and in the participant's own home (B?). Findings - Interactive verbal prompting technology (Guide) significantly reduced support worker prompting and number of errors in the in-patient setting and in the participant's own home. Research limitations/implications - The results suggest that interactive verbal prompting can be used to support motivational tasks such as getting out of bed and the morning routine. This study used a single subject experimental design and the results need to be confirmed in a larger sample. Originality/value - This is the first report of use of interactive verbal prompting technology to support rehabilitation of a motivational task. It is also the first study to evaluate Guide in a domestic context

Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation.

After an initial period of recovery, human neurological injury has long been thought to be static. In order to improve quality of life for those suffering from stroke, spinal cord injury, or traumatic brain injury, researchers have been working to restore the nervous system and reduce neurological deficits through a number of mechanisms. For example, neurobiologists have been identifying and manipulating components of the intra- and extracellular milieu to alter the regenerative potential of neurons, neuro-engineers have been producing brain-machine and neural interfaces that circumvent lesions to restore functionality, and neurorehabilitation experts have been developing new ways to revitalize the nervous system even in chronic disease. While each of these areas holds promise, their individual paths to clinical relevance remain difficult. Nonetheless, these methods are now able to synergistically enhance recovery of native motor function to levels which were previously believed to be impossible. Furthermore, such recovery can even persist after training, and for the first time there is evidence of functional axonal regrowth and rewiring in the central nervous system of animal models. To attain this type of regeneration, rehabilitation paradigms that pair cortically-based intent with activation of affected circuits and positive neurofeedback appear to be required-a phenomenon which raises new and far reaching questions about the underlying relationship between conscious action and neural repair. For this reason, we argue that multi-modal therapy will be necessary to facilitate a truly robust recovery, and that the success of investigational microscopic techniques may depend on their integration into macroscopic frameworks that include task-based neurorehabilitation. We further identify critical components of future neural repair strategies and explore the most updated knowledge, progress, and challenges in the fields of cellular neuronal repair, neural interfacing, and neurorehabilitation, all with the goal of better understanding neurological injury and how to improve recovery

Real-time Hybrid Locomotion Mode Recognition for Lower-limb Wearable Robots

Real-time recognition of locomotion-related activities is a fundamental skill that the controller of lower-limb wearable robots should possess. Subject-specific training and reliance on electromyographic interfaces are the main limitations of existing approaches. This study presents a novel methodology for real-time locomotion mode recognition of locomotion-related activities in lower-limb wearable robotics. A hybrid classifier can distinguish among seven locomotion-related activities. First, a time-based approach classifies between static and dynamical states based on gait kinematics data. Second, an event-based fuzzy logic method triggered by foot pressure sensors operates in a subject-independent fashion on a minimal set of relevant biomechanical features to classify among dynamical modes. The locomotion mode recognition algorithm is implemented on the controller of a portable powered orthosis for hip assistance. An experimental protocol is designed to evaluate the controller performance in an out-of-lab scenario without the need for a subject-specific training. Experiments are conducted on six healthy volunteers performing locomotion-related activities at slow, normal, and fast speeds under the zero-torque and assistive mode of the orthosis. The overall accuracy rate of the controller is 99.4% over more than 10,000 steps, including seamless transitions between different modes. The experimental results show a successful subject-independent performance of the controller for wearable robots assisting locomotion-related activities

Suggested approach for establishing a rehabilitation engineering information service for the state of California

An ever expanding body of rehabilitation engineering technology is developing in this country, but it rarely reaches the people for whom it is intended. The increasing concern of state and federal departments of rehabilitation for this technology lag was the stimulus for a series of problem-solving workshops held in California during 1977. As a result of the workshops, the recommendation emerged that the California Department of Rehabilitation take the lead in the development of a coordinated delivery system that would eventually serve the entire state and be a model for similar systems across the nation

Towards Power-Efficient Design of Myoelectric Controller based on Evolutionary Computation

Myoelectric pattern recognition is one of the important aspects in the design of the control strategy for various applications including upper-limb prostheses and bio-robotic hand movement systems. The current work has proposed an approach to design an energy-efficient EMG-based controller by considering a supervised learning framework using a kernelized SVM classifier for decoding the information of surface electromyography (sEMG) signals to infer the underlying muscle movements. In order to achieve the optimized performance of the EMG-based controller, our main strategy of classifier design is to reduce the false movements of the overall system (when the EMG-based controller is at the `Rest' position). To this end, unlike the traditional single training objective of soft margin kernelized SVM, we have formulated the training algorithm of the proposed supervised learning system as a general constrained multi-objective optimization problem. An elitist multi-objective evolutionary algorithm $-$ the non-dominated sorting genetic algorithm II (NSGA-II) has been used for the tuning of SVM hyperparameters. We have presented the experimental results by performing the experiments on a dataset consisting of the sEMG signals collected from eleven subjects at five different upper limb positions. It is evident from the presented result that the proposed approach provides much more flexibility to the designer in selecting the parameters of the classifier to optimize the energy efficiency of the EMG-based controller.Comment: Submitted to IEEE Journa

A Survey of Brain Computer Interface Using Non-Invasive Methods

Research on Brain-Computer Interface (BCI) began in the 1970s and has increased in volume and diversified significantly since then. Today BCI is widely used for applications like assistive devices for physically challenged users, mental state monitoring, input devices for hands-free applications, marketing, education, security, games and entertainment. This article explores the advantages and disadvantages of invasive and non-invasive BCI technologies and focuses on use cases of several non-invasive technologies, namely electroencephalogram (EEG), functional Magnetic Resonance Imaging (fMRI), Near Infrared Spectroscopy (NIRs) and hybrid systems

Haptic wearables as sensory replacement, sensory augmentation and trainer - a review

Sensory impairments decrease quality of life and can slow or hinder rehabilitation. Small, computationally powerful electronics have enabled the recent development of wearable systems aimed to improve function for individuals with sensory impairments. The purpose of this review is to synthesize current haptic wearable research for clinical applications involving sensory impairments. We define haptic wearables as untethered, ungrounded body worn devices that interact with skin directly or through clothing and can be used in natural environments outside a laboratory. Results of this review are categorized by degree of sensory impairment. Total impairment, such as in an amputee, blind, or deaf individual, involves haptics acting as sensory replacement; partial impairment, as is common in rehabilitation, involves haptics as sensory augmentation; and no impairment involves haptics as trainer. This review found that wearable haptic devices improved function for a variety of clinical applications including: rehabilitation, prosthetics, vestibular loss, osteoarthritis, vision loss and hearing loss. Future haptic wearables development should focus on clinical needs, intuitive and multimodal haptic displays, low energy demands, and biomechanical compliance for long-term usage