This paper presents the conceptualization, design and development of a bioinspired electronic architecture based on the human motor system for the control of mobility assistance systems, such as assistance or rehabilitation exoskeletons. The proposed architecture is divided into three hierarchical levels: perception-intention, pattern generator, and execution, facilitating modularity, scalability, and parallelism in the execution and operation of the system. ROS2 was chosen as the middleware for communication management due to its ability to handle a large amount of data, robustness, and scalability. For validation in a real world scenario, the proposal was implemented in the gait rehabilitation robotic platform Discover2Walk. Among the advantages offered by this architecture, we highlight greater modularity, improved compatibility with programming languages, and scalability, in addition to ease of supervision and control. The architecture presented can be adopted in future robotic platforms and exoskeletons with built-in interoperability.

Recent advances in the control of overground exoskeletons are being centered on improving balance support and decreasing the reliance on crutches. However, appropriate methods to quantify the stability of these exoskeletons (and their users) are still under development. A reliable and reproducible balance assessment is critical to enrich exoskeletons’ performance and their interaction with humans. In this work, we present the BenchBalance system, which is a benchmarking solution to conduct reproducible balance assessments of exoskeletons and their users. Integrating two key elements, i.e., a hand-held perturbator and a smart garment, BenchBalance is a portable and low-cost system that provides a quantitative assessment related to the reaction and capacity of wearable exoskeletons and their users to respond to controlled external perturbations. A software interface is used to guide the experimenter throughout a predefined protocol of measurable perturbations, taking into account antero-posterior and mediolateral responses. In total, the protocol is composed of sixteen perturbation conditions, which vary in magnitude and location while still controlling their orientation. The data acquired by the interface are classified and saved for a subsequent analysis based on synthetic metrics. In this paper, we present a proof of principle of the BenchBalance system with a healthy user in two scenarios: subject not wearing and subject wearing the H2 lower-limb exoskeleton. After a brief training period, the experimenter was able to provide the manual perturbations of the protocol in a consistent and reproducible way. The balance metrics defined within the BenchBalance framework were able to detect differences in performance depending on the perturbation magnitude, location, and the presence or not of the exoskeleton. The BenchBalance system will be integrated at EUROBENCH facilities to benchmark the balance capabilities of wearable exoskeletons and their users.

This paper presents a development of the smart garment instrumented with polymer optical fiber (POF) sensors to identify the impact location during a perturbation protocol for balance assessment. The sensor system installed on the smart garment consists of 30 multiplexed sensors, and each sensor is composed of a light-emitting diode (LED) coupled to a lateral cut in the POF. The sensor’s responses are acquired by four photodetectors and the data processing is made by a microcontroller. Two tests were performed to characterize the system. First, forces were applied on each sensor using a reference force sensor to characterize their sensitivities. Second, several perturbations on predefined body areas were performed. A technique based on the sensors’ responses is proposed to identify impacts. Finally, the proposed system was applied on a perturbation protocol to assess the human balance under an instability condition. Results of the first test showed different sensor sensitivities due to differences provoked by the manufacturing process, and the sensors were normalized. Results of the second test showed the ability of identifying the impact region by using the impact location identification technique. Furthermore, results of the perturbation protocol showed the feasibility of the proposed system in the balance assessment, in which the identified impact regions combined with the trunk angles obtained by an inertial measurement unit (IMU) improved the balance assessment. The proposed system is portable, low cost, with simple signal processing and ease of implementation, with possibility of scalability, in which can be adapted for other impact detection protocols.

Stroke survivors usually exhibit concurrent motor and cognitive impairment. Historically, rehabilitation strategies post-stroke occur separately in terms of motor and cognitive functions. However, recent studies show that hand motor interventions can have a positive impact on cognitive recovery. In this work, we introduce AMBER (portAble and Modular device for comprehensive Brain Evaluation and Rehabilitation), a new device developed for the evaluation and rehabilitation of both hand motor function and cognition simultaneously. AMBER is a simple, portable, ergonomic and cheap device based on Force Sensitive Resistors, in which every finger interaction is recorded to provide information about finger strength, processing speed, and memory status. This paper presents the requirements of the device and the design of the system. In addition, a pilot study was conducted with 36 healthy individuals using the evaluation module of the device to assess its psychometric properties, as test-retest reliability and measurement error. Its validity was also evaluated comparing its measurements with three different gold standards for strength, processing speed and memory. The device showed good test-retest reliability for strength (ICC =0.741-0.852), reaction time (ICC =0.715 – 0.900) and memory (ICC =0.556-0.885). These measures were correlated with their corresponding gold standards (r =0.780-890). AMBER shows great potential to impact hand rehabilitation, offering therapists a valid, reliable and versatile tool to comprehensively assess patients. With ongoing advancements and refinements, it has the opportunity to significantly impact rehabilitation practices and improve patient outcomes.