Recent advancements in gait rehabilitation have led to the development of innovative approaches that complement traditional therapeutic methods. These include intense, task-specific exercise strategies, non-invasive treatments, surgical interventions, and advanced robotic technologies. While robotic systems for adult gait rehabilitation are well-established, there is a notable scarcity of such devices for pediatric patients, especially toddlers, due to their unique developmental and biomechanical needs. This work introduces Discover2Walk (D2W), a novel robotic platform designed specifically for pediatric gait rehabilitation in small children. The D2W platform features a multi-module, cable-driven architecture guided by an omnidirectional traction module, addressing the limitations of current rehabilitation devices for younger populations. The platform's modular design consists of three actuated modules—pelvic, ankle, and traction—synchronized by a personalised gait pattern generator. This configuration allows for simultaneous control and monitoring of pelvic and ankle motion using partial body weight support and Assistance As Needed (AAN) approaches. Preliminary evaluations were conducted with pediatric patients with Cerebral Palsy, involving two ambulatory six-year-olds and one non-ambulatory four-year-old, over a series of 10 gait rehabilitation sessions. Data analysis from the ambulatory children showed a decrease in the robotic effort required to assist limb movements along healthy trajectories throughout the sessions, accompanied by an increase in walking speeds. Further work will include expanding the patient cohort to include a broader range of ages, sizes and GMFCS levels to validate the system's effectiveness across a wider spectrum of pediatric gait disabilities and validating the traction effectiveness.

Self-modulation of brain signals related to motor imagery (MI) activity has been increasingly indicated to be beneficial for motor rehabilitation protocols. Appropriately incorporating the MI strategy requires ensuring the active involvement of the central nervous system. Under this context, seeking MIrelated features to be easily modulated by the user and that are recurrent between distinct MI sessions is a necessary and challenging task. This paper proposes a criterion to maximize the consistency of features from the electroencephalography signal between recording sessions performed at different days. To achieve this, we explore three distinct approaches to rank features related to MI's underlying mental patterns - the event-related desynchronization (ERD) score, the ERD occurrence (EO) and the Fisher ratio- applied to a database of 3 healthy adults and 4 children with cerebral palsy. Overall, the healthy adults presented more consistent features for F-, FC- and C-labeled electrodes and the lower frequency bands for the EO and ERD score, and higher ranges for the Fisher ratio. For the children, results showed more variability but emphasis on specific spectral and spatial locations could also be observed individually. Hence, we claim that distinct ranking metrics may reflect different aspects of MI, but each one may be best for specific types of applications and subjects.

The advances and fast spread of mobile devices and technologies, we witness today, have extended its advantages over medical and health practice supported by mobile devices, giving rise to the growing research of Internet of Things (IoT), especially the e-Health field. The features provided by mobile technologies revealed to be of major importance when we consider the continuous aging of population and the consequent increase of its debilities. In addition to the increase of lifetime span of population, also the increase of health risks and their locomotive impairments increases, requiring a close monitoring and continuous evaluation. Such monitoring should be as noninvasive as possible, in order not to compromise the mobility and the day-to-day activities of citizens. Therefore, we present the development of a noninvasive optical fiber sensor (OFS) architecture adaptable to a shoe sole for plantar pressure remote monitoring, which is suitable to be integrated in an IoT e-Health solution to monitor the well-being of individuals. This paper explores the production of the OFS multiplexed network (using fiber Bragg gratings) to monitor the foot plantar pressure distribution during gait (walking movement). From the acquired gait data, it is possible to infer health conditions of the patient's foot and spine posture. To guarantee the patients mobility, the proposed system consists of an OFS network integrated with a wireless transceiver to enable efficient ubiquitous monitoring of patients. This paper shows the calibration and measurement results, which reflect the accuracy of the proposed system, under normal walking in controlled area.

The continuous aging of the population has increased health risks, leading to the need for close monitoring of elder citizens and patients. Fortunately, the field of e-Health has been experiencing great progress, although it still faces challenges, since it needs to be applied without compromising the mobility or lifestyle of monitored patients; i.e. non-invasive. Addressing such challenges, we present a non-invasive energy- efficient insole optical fiber sensor architecture for plantar pressure monitoring. The paper shows the design and implementation of a non-invasive "in-sole" optical fiber sensor network (using Fiber Bragg Gratings), which is able to monitor health conditions by observing plantar pressure distribution in the foot. The system integrates the optical fiber sensor network with an energy efficient wireless transceiver, in order to allow continuous monitoring of monitored patients, during their uninterrupted daily routine. The paper presents the calibration and laboratory measurements of the sensing network. The recorded measurements show the suitability and high accuracy of the proposed system in monitoring the plantar pressure distribution during walking movement (i.e. gait).

This article evaluates the performance of individuals with cerebral palsy (CP) using an inertial human–computer interface (HCI) based on head movements and a novel facilitation algorithm which we named MouseField. In total, 17 participants from three centers specialized in CP participated in a series of tests that evaluated rapidity and accuracy in pointing tasks using a head mouse named ENLAZA, with and without MouseField. Cluster analysis was used to classify the participants into three categories, C1–C3 ordered by throughput. MouseField improved rapidity in C2 and C3 and accuracy in C3. Although the accuracy of the least impaired (C1, MACS IV–V) decreased, MouseField improved task performance of users with severe motor disorders (MACS V), with decrements of 33.3 and 65.5% in the task execution time. The results show that severely impaired individuals who cannot use traditional input devices could benefit from using MouseField and ENLAZA for a facilitated navigation that minimizes targeting errors.

Cerebral Palsy (CP) is a disorder of posture and movement due to an imperfection or lesion in the immature brain. CP is often associated to sensory deficits, cognition impairments, communication and motor disabilities, behaviour issues, seizure disorder, pain and secondary musculoskeletal problems. New strategies are needed to help to promote, maintain, and rehabilitate the functional capacity, and thereby diminish the dedication and assistance required and the economical demands that this condition represents for the patient, the caregivers and the whole society. This paper describes the conceptualization and development of the integrated CPWalker robotic platform to support novel therapies for CP rehabilitation. This platform (Smart Walker + exoskeleton) is controlled by a multimodal interface to establish the interaction of CP children with robot-based therapies. The objective of these therapies is to improve the physical skills of children with CP and similar disorders. CPWalker concept will promote the earlier incorporation of CP patients to the rehabilitation therapy and increase the level of intensity and frequency of the exercises according to the task, which will enable the maintenance of therapeutic methods in daily basis, with the intention to lead to significant improvements in the treatment outcome.

Every year, stroke significantly affects thousands of individuals who require rehabilitation to regain lost movements and return to a normal life. Exoskeletons are becoming a very powerful tool to help therapists in this rehabilitation process. This work presents a robotic exoskeleton designed to assist overground gait training for stroke survivors with deficits in gait coordination despite conventional rehabilitation. The device is a bilateral exoskeleton with six degrees of freedom and is designed to implement two control strategies. An adaptive trajectory control has been developed to guide the patient's limb within a desired path, allowing a deviation based on torque of interaction between the user and the exoskeleton. An admittance control strategy allows the robotic platform to capture the user's movements during assistive training and to replicate it during active training. Experimental results show that the exoskeleton can adapt a pre-recorded gait pattern for the gait pattern of a specific user. Future investigations will evaluate the device in the rehabilitation of patients who have suffered from stroke and make a comparative analysis of the effectiveness of different robotic therapies.

Tremor is the most prevalent movement disorder, and its incidence is increasing with aging. In spite of the numerous therapeutic solutions available, 65% of those suffering from upper limb tremor report serious difficulties during their daily living. This gives rise to research on different treatment alternatives, amongst which wearable robots that apply selective mechanical loads constitute an appealing approach. In this context, the current work presents a multimodal human-robot interface to drive a neuroprosthesis for tremor management. Our approach relies on the precise characterization of the tremor to modulate a functional electrical stimulation system that compensates for it. The neuroprosthesis is triggered by the detection of the intention to move derived from the analysis of electroencephalographic activity, which provides a natural interface with the user. When a prediction is delivered, surface electromyography serves to detect the actual onset of the tremor in the presence of volitional activity. This information in turn triggers the stimulation, which relies on tremor parameters-amplitude and frequency-derived from a pair of inertial sensors that record the kinematics of the affected joint. Surface electromyography also yields a first characterization of the tremor, together with precise information on the preferred stimulation site. Apart from allowing for an optimized performance of the system, our multimodal approach permits the implementation of redundant methods to both enhance the reliability of the system and adapt to the specific needs of different users. Results with a representative group of patients illustrate the performance of the interface presented here and demonstrate its feasibility.

Tremor constitutes the most common motor disorder, and poses a functional problem to a large number of patients. Despite of the considerable experience in tremor management, current treatment based on drugs or surgery does not attain an effective attenuation in 25 % of patients, motivating the need for research in new therapeutic alternatives. In this context, this paper presents the concept design, development, and preliminary validation of a soft wearable robot for tremor assessment and suppression. The TREMOR neurorobot comprises a Brain Neural Computer Interface that monitors the whole neuromusculoskeletal system, aiming at characterizing both voluntary movement and tremor, and a Functional Electrical Stimulation system that compensates for tremulous movements without impeding the user perform functional tasks. First results demonstrate the performance of the TREMOR neurorobot as a novel means of assessing and attenuating pathological tremors.

The computer is considered a very useful tool for physical and cognitive rehabilitation. However, people with disabilities have limitations to control properly conventional user interfaces because of their motor diseases. This paper is in the framework of a project to create an alternative computer interface, especially addressed to children with Cerebral Palsy (CP). The interface is a head mouse based on inertial technology. Although the inertial interface succeeds as mouse pointer for healthy users, children with CP have involuntary movements, such as spasms or tremor, which limit the control. In this paper, the inertial interface is used to analyze the pathological patterns of three children with CP (hypertonic-athetoid, hypertonic-dystonic and hypotonic-athetoid cases) in terms of kinematic parameters and spectral analysis of the pathological motion. Finally, two filtering techniques are designed and evaluated to reduce the effects of the involuntary motion and improve the accessibility of the interface as mouse pointer.

Pathological tremor constitutes the most common movement disorder, and is increasing its prevalence with ageing. Treatment forms range from drugs to surgery in those patients refractory to drugs, however, tremor is not effectively managed in about 25% of patients. According to this, new management techniques such as wearable robots that take advantage of selective biomechanical loading seem an interesting alternative. Our objective is to design robotic exoskeletons which suppress tremor, letting the user perform a voluntary movement, by means of intelligent control approaches that include accurate tremor models. In this context, we propose a two-stage algorithm for real-time estimation of time varying tremor amplitude and frequency. It is based on the assumption that tremor alters voluntary motion in an additive manner, and happens in a higher frequency band. The two-stage algorithm first generates an estimation of voluntary movement based on its inherent slower dynamics, and then removes it from the total motion, directly providing an estimate of tremor. This tremor estimation is then fed into an adaptive filter, which provides instantaneous tremor characteristics. Accurate and robust tremor amplitude and frequency estimates are obtained.

Biomechanical loading, in particular, viscous loading of the upper limb has been proposed in the literature as a means for suppressing pathologic tremor. It is expected that an improvement on manipulative function can be obtained by reducing the tremorous motion associated to some neurological disorders. This article presents two non-grounded control strategies to suppress tremor by means of a orthotic (wearable) exoskeleton. These two strategies are based on biomechanical loading and notch filtering of tremor via internal forces. Both controls strategies are evaluated and validated on the robotic exoskeleton called WOTAS (wearable orthosis for tremor assessment and suppression). At the end, results obtained in the pre-clinical trials and conclusions of this study are presented

There is a need for wearable powered upper limb exoskeletons able to apply forces to the upper limb for use by people with disabilities and/or limb weakness or injury. The robotic exoskeleton called WOTAS (Wearable Orthosis for Tremor Assessment and Suppression) presented in this paper will provide a means of testing non-grounded control strategies in order to help these people. For instance, biomechanical loading, in particular, viscous loading of the upper limb has been proposed in the literature as a means for suppressing pathologic tremor. This article describes in detail the general concept for WOTAS, outlining the special features of the design and selection of system components.

The present paper address the analysis of Ultrasonic motors, UM, as driving technology in impedance controlled robotic manipulators. A focus is given on rehabilitation robotics as an emerging field in which impedance controlled approaches are more suited. The article gives some general remarks on impedance control, briefly introduces a UM driven miniature SCARA robot and describes a model of the manipulator and UM that serves the goal of gaining insight in the expected behavior of the controlled system. The experimental results and evaluation of UM as driving technology make use of a set of figures of merit previously defined. Eventually, some concluding remarks are given.