Intention recognition for FES in a grasp-and-release task using volitional EMG and inertial sensors

Functional Electrical Stimulation (FES) facilitates the motor recovery of the hand function after stroke. The integration of biofeedback and other strategies to actively involve a patient in the therapy is important for the rehabilitation progress. We introduce a combined control approach for a FES-driven neuroprosthesis using volitional electromyo-graphy (vEMG) and motion capturing via a novel inertial sensor network for patients that still possess a residual activity in the paralyzed muscles. A real-time vEMG measurement and signal processing in between stimulation pulses has been realized during active FES. Experiments showed that our system allows for quick adaption to individual users.BMBF, 16SV7069K, Verbundprojekt: Bewegungsfähigkeit und Mobilität wiedererlangen - BeMobil -; Teilvorhaben: Nutzerzentrierte Entwicklung technischer Methoden für eine optimale Mensch-Technik-Interaktion in der Bewegungsrehabilitatio

A new semi-automatic approach to find suitable virtual electrodes in arrays using an interpolation strategy

Functional Electrical Stimulation via electrode arrays enables the user to form virtual electrodes (VEs) of dynamic shape, size, and position. We developed a feedback-control-assisted manual search strategy which allows the therapist to conveniently and continuously modify VEs to find a good stimulation area. This works for applications in which the desired movement consists of at least two degrees of freedom. The virtual electrode can be moved to arbitrary locations within the array, and each involved element is stimulated with an individual intensity. Meanwhile, the applied global stimulation intensity is controlled automatically to meet a predefined angle for one degree of freedom. This enables the therapist to concentrate on the remaining degree(s) of freedom while changing the VE position. This feedback-control-assisted approach aims to integrate the user's opinion and the patient's sensation. Therefore, our method bridges the gap between manual search and fully automatic identification procedures for array electrodes. Measurements in four healthy volunteers were performed to demonstrate the usefulness of our concept, using a 24-element array to generate wrist and hand extension.BMBF, 16SV7069K, Verbundprojekt: Bewegungsfähigkeit und Mobilität wiedererlangen - BeMobil -; Teilvorhaben: Nutzerzentrierte Entwicklung technischer Methoden für eine optimale Mensch-Technik-Interaktion in der Bewegungsrehabilitatio

A Tangible Solution for Hand Motion Tracking in Clinical Applications

Objective real-time assessment of hand motion is crucial in many clinical applications including technically-assisted physical rehabilitation of the upper extremity. We propose an inertial-sensor-based hand motion tracking system and a set of dual-quaternion-based methods for estimation of finger segment orientations and fingertip positions. The proposed system addresses the specific requirements of clinical applications in two ways: (1) In contrast to glove-based approaches, the proposed solution maintains the sense of touch. (2) In contrast to previous work, the proposed methods avoid the use of complex calibration procedures, which means that they are suitable for patients with severe motor impairment of the hand. To overcome the limited significance of validation in lab environments with homogeneous magnetic fields, we validate the proposed system using functional hand motions in the presence of severe magnetic disturbances as they appear in realistic clinical settings. We show that standard sensor fusion methods that rely on magnetometer readings may perform well in perfect laboratory environments but can lead to more than 15 cm root-mean-square error for the fingertip distances in realistic environments, while our advanced method yields root-mean-square errors below 2 cm for all performed motions.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Modular finger and hand motion capturing system based on inertial and magnetic sensors

The assessment of hand posture and kinematicsis increasingly important in various fields. This includesthe rehabilitation of stroke survivors with restricted handfunction. This paper presents a modular, ambulatory mea-surement system for the assement of the remaining handfunction and for closed-loop controlled therapy. The de-vice is based on inertial sensors and utilizes up to fiveinterchangeable sensor strips to achieve modularity and tosimplify the sensor attachment. We introduce the modularhardware design and describe algorithms used to calculatethe joint angles. Measurements with two experimentalsetups demonstrate the feasibility and the potential of such a tracking device.BMBF, 16SV7069K, Verbundprojekt: Bewegungsfähigkeit und Mobilität wiedererlangen - BeMobil -; Teilvorhaben: Nutzerzentrierte Entwicklung technischer Methoden für eine optimale Mensch-Technik-Interaktion in der Bewegungsrehabilitatio

Algorithms for Automated Calibration of Transcutaneous Spinal Cord Stimulation to Facilitate Clinical Applications

Transcutaneous spinal cord stimulation (tSCS) is a promising intervention that can benefit spasticity control and augment voluntary movement in spinal cord injury (SCI) and multiple sclerosis. Current applications require expert knowledge and rely on the thorough visual analysis of electromyographic (EMG) responses from lower-limb muscles to optimize attainable treatment effects. Here, we devised an automated tSCS setup by combining an electrode array placed over low-thoracic to mid-lumbar vertebrae, synchronized EMG recordings, and a self-operating stimulation protocol to systematically test various stimulation sites and amplitudes. A built-in calibration procedure classifies the evoked responses as reflexes or direct motor responses and identifies stimulation thresholds as recommendations for tSCS therapy. We tested our setup in 15 individuals (five neurologically intact, five SCI, and five Parkinson’s disease) and validated the results against blinded ratings from two clinical experts. Congruent results were obtained in 13 cases for electrode positions and in eight for tSCS amplitudes, with deviations of a maximum of one position and 5 to 10 mA in amplitude in the remaining cases. Despite these minor deviations, the calibration found clinically suitable tSCS settings in 13 individuals. In the remaining two cases, the automatic setup and both experts agreed that no reflex responses could be detected. The presented technological developments may facilitate the dissemination of tSCS into non-academic environments and broaden its use for diagnostic and therapeutic purposes.DFG, 424778381, Behandlung motorischer Netzwerkstörungen mittels NeuromodulationDFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berli

Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders

The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments.DFG, 424778381, Behandlung motorischer Netzwerkstörungen mittels Neuromodulatio

Real-Time Detection of Freezing Motions in Parkinson's Patients for Adaptive Gait Phase Synchronous Cueing

Parkinson's disease is the second most common neurodegenerative disease worldwide reducing cognitive and motoric abilities of affected persons. Freezing of Gait (FoG) is one of the severe symptoms that is observed in the late stages of the disease and considerably impairs the mobility of the person and raises the risk of falls. Due to the pathology and heterogeneity of the Parkinsonian gait cycle, especially in the case of freezing episodes, the detection of the gait phases with wearables is challenging in Parkinson's disease. This is addressed by introducing a state-automaton-based algorithm for the detection of the foot's motion phases using a shoe-placed inertial sensor. Machine-learning-based methods are investigated to classify the actual motion phase as normal or FoG-affected and to predict the outcome for the next motion phase. For this purpose, spatio-temporal gait and signal parameters are determined from the segmented movement phases. In this context, inertial sensor fusion is applied to the foot's 3D acceleration and rate of turn. Support Vector Machine (SVM) and AdaBoost classifiers have been trained on the data of 16 Parkinson's patients who had shown FoG episodes during a clinical freezing-provoking assessment course. Two clinical experts rated the video-recorded trials and marked episodes with festination, shank trembling, shuffling, or akinesia. Motion phases inside such episodes were labeled as FoG-affected. The classifiers were evaluated using leave-one-patient-out cross-validation. No statistically significant differences could be observed between the different classifiers for FoG detection (p>0.05). An SVM model with 10 features of the actual and two preceding motion phases achieved the highest average performance with 88.5 ± 5.8% sensitivity, 83.3 ± 17.1% specificity, and 92.8 ± 5.9% Area Under the Curve (AUC). The performance of predicting the behavior of the next motion phase was significantly lower compared to the detection classifiers. No statistically significant differences were found between all prediction models. An SVM-predictor with features from the two preceding motion phases had with 81.6 ± 7.7% sensitivity, 70.3 ± 18.4% specificity, and 82.8 ± 7.1% AUC the best average performance. The developed methods enable motion-phase-based FoG detection and prediction and can be utilized for closed-loop systems that provide on-demand gait-phase-synchronous cueing to mitigate FoG symptoms and to prevent complete motoric blockades.BMBF, 16SV8168, Verbundprojekt: Mobilitätsassistent für Parkinsonpatienten - Mobil4Park -; Teilvorhaben: On-Demand Stimulationssystem mit Tele-Medizin-FunktionDFG, 424778381, Behandlung motorischer Netzwerkstörungen mittels NeuromodulationDFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berli

Eine adaptive Handneuroprothese unter Verwendung von Inertialsensoren zur Bewegungserfassung in Echtzeit

People suffering from upper limb impairments after a stroke or spinal cord injury are not only restricted in their independence but also in their inclusion in professional and social life. The increasing number of patients and the resulting rise in timely and monetary rehabilitation expenses lead to strong demands for new, effective therapies. Neuroprostheses based on functional electrical stimulation (FES) have been found to influence motor recovery positively. Electrical pulses are applied to peripheral nerves in the forearm and hand to generate functional hand motions. However, noninvasive hand neuroprostheses (HNPs) for rehabilitation face several challenges in clinical practice. The limited selectivity of transcutaneous FES yields difficulties in achieving fine hand movements by stimulating the muscle-rich forearm. Intersubject variability in neuroanatomy and tolerance of the FES make an individual adjustment of spatial and temporal stimulation parameters obligatory. Furthermore, strategies are required for a quick and easy adaptation of stimulation parameters in real-time, as the neuromuscular system is subject to time-variant changes. In this thesis, new concepts and methods are presented on the road to a novel, adaptive HNP based on automation, closed-loop control, and user-centered design. The HNP features a new, modular hand sensor system for accurate real-time motion tracking of FES-induced movements. In contrast to glove-based approaches, the proposed solution maintains the sense of touch. Algorithms for measuring segment orientations, wrist and finger joint angles, and fingertip positions from up to 17 micro inertial sensors were developed for application in patients with severe motor impairment of the hand. The methods avoid extensive calibration movements performed by the patients and work robustly in magnetically disturbed environments, i.e., indoors. The sensor system was evaluated with four healthy subjects in different validation settings before it was applied in clinical studies. Selective and individual stimulation of hand motion was assured by utilizing electrode arrays for the HNP together with user-centered identification strategies. An effective search for suitable virtual electrodes, formed by multiple, active array elements, is essential for clinical acceptance and practicability of HNPs. Semi-automatic and automatic methods for identifying stimulation positions and intensities were developed, realizing different levels of user integration. The semi-automatic approach allows caregivers to continuously modify virtual electrodes via a touchscreen while the stimulation intensities are automatically controlled to achieve desired wrist extension. Both identification methods were evaluated in five stroke survivors and yield suitable stimulation setups for hand opening and closing in patients who could tolerate the FES, with the semi-automatic approach being 25% faster than the automatic. A static parameter setup throughout a therapy session does not account for changes in the muscular response. For example, the rotation of the forearm during reach-and-grasp tasks leads to a change in FES response due to the relative transition between the skin and underlying neuromuscular tissues. An automatic real-time adaptation strategy of virtual electrodes and stimulation intensity in electrode arrays was investigated for a secure grasp during forearm movements. The novel method facilitates dynamic repositioning of electrodes and optional closed-loop control of the stimulation intensity. The hand sensor system was used to estimate grasping strength when using elastic objects. Experiments in four able-bodied volunteers revealed that the automatic electrode adaptation generates a strong, stable grasp force regardless of the rotational state of the forearm, in contrast to static electrodes. In summary, the presented concepts and methods in this thesis contribute to a higher degree of automation and adaptation of HNPs, which in the long run will enhance the use of FES-based technology in rehabilitation and, thereby, promote the motor recovery of patients.Menschen mit Lähmungserscheinungen in den oberen Extremitäten nach einem Schlaganfall oder einer Rückenmarksverletzung sind sowohl in ihrer Unabhängigkeit als auch in ihrer beruflichen und sozialen Integration eingeschränkt. Wachsende Patientenzahlen und der damit verbundene Anstieg des zeitlichen und finanziellen Aufwands für die Rehabilitation führen zu einer starken Nachfrage nach neuen Therapien. Neuroprothesen basierend auf funktioneller Elektrostimulation (FES) können das Wiedererlangen motorischer Fähigkeiten positiv beeinflussen. Funktionelle Handbewegungen werden durch elektrische Anregung der peripheren Nerven im Unterarm erzeugt. Nicht-invasive Handneuroprothesen (HNPs) stehen jedoch in der Praxis vor diversen Herausforderungen. Die eingeschränkte Selektivität der transkutanen FES gestaltet die Erzeugung feinmotorischer Bewegungen durch Stimulation am muskelreichen Unterarm schwierig. Variabilität in Neuroanatomie und Toleranz gegenüber FES machen eine individuelle Anpassung der Stimulationsparameter erforderlich. Darüber hinaus sind Strategien zur schnellen, einfachen Adaption der FES in Echtzeit notwendig, da das neuromuskuläre System zeitvariablen Veränderungen unterworfen ist. In dieser Dissertation werden neue Konzepte und Methoden für eine neuartige, adaptive HNP vorgestellt basierend auf Automatisierung, Regelung und benutzerzentriertem Design. Die HNP verfügt über ein neues, modulares Handsensorsystem für die Erfassung FES-induzierter Bewegungen in Echtzeit. Im Gegensatz zu handschuhbasierten Ansätzen bewahrt die vorgeschlagene Sensorik den Tastsinn. Für die Anwendung bei Patienten mit schweren motorischen Beeinträchtigungen der Hand wurden Algorithmen zur Messung von Orientierungen, Hand- und Fingergelenkwinkeln sowie Positionen der Fingerspitzen durch bis zu 17 Inertialsensoren entwickelt. Die Verfahren vermeiden komplexe Kalibrierungsbewegungen, die von den Patienten ausgeführt werden müssen, und arbeiten robust in magnetisch gestörten Umgebungen, d. h. auch in Innenräumen. Das Sensorsystem wurde mit vier gesunden Probanden in unterschiedlichen Set-ups validiert, bevor es in den folgenden Studien zum Einsatz kam. Eine selektive und patienten-individuelle Stimulation von Handbewegungen wurde durch die Verwendung von Elektrodenarrays und benutzerfreundlichen Identifikationsstrategien in der neuen HNP erreicht. Eine effektive Suche nach geeigneten virtuellen Elektroden, die aus aktiven Array-Elementen bestehen, ist für die klinische Akzeptanz und Praktikabilität entscheidend. Es wurden halb- sowie voll-automatische Methoden zur Identifikation von Stimulationspositionen und -intensitäten entwickelt, um verschiedene Ebenen der Benutzerintegration zu realisieren. Der halb-automatische Ansatz ermöglicht es dem Anwender, virtuelle Elektroden über einen Touchscreen kontinuierlich zu modifizieren, während die Stimulationsintensitäten automatisch geregelt werden. Beide Identifikationsmethoden wurden in fünf Schlaganfallpatienten evaluiert und geeignete virtuelle Elektroden für Handöffnen und -schließen gefunden, sofern die Patienten die FES tolerierten. Der halb-automatische Ansatz war 25% schneller als der automatische. Bei Verwendung statischer Parametereinstellungen während einer Therapiesitzung werden Änderungen in der Muskelantwort nicht berücksichtigt. Beispielsweise beeinflusst die Drehung des Unterarms die FES-induzierte Handbewegung aufgrund der relativen Verschiebung zwischen Haut und darunter liegendem neuromuskulären Gewebe. In der Arbeit wird eine automatische Echtzeitadaption für virtuelle Elektroden und FES-Intensität in Elektrodenarrays vorgestellt, um einen sicheren Griff bei Unterarmbewegungen zu gewährleisten. Das neue Verfahren ermöglicht die dynamische Neupositionierung von aktiven Elektroden und optional die Regelung der Stimulationsintensität. Mit dem neuen Handsensorsystem wird die Greifkraft bei Verwendung elastischer Objekte abgeschätzt. Experimente mit vier gesunden Probanden ergaben, dass die automatische Adaption im Gegensatz zu statischen aktiven Elektroden eine stabile Greifkraft unabhängig vom Rotationszustand des Unterarms erzeugt. Zusammenfassend ermöglichen die vorgestellten Konzepte und Methoden einen höheren Grad an Automatisierung und Anpassung von HNPs, was langfristig den Einsatz dieser Technologie in der Rehabilitation und dadurch die motorische Genesung fördern kann

Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders

The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments

Algorithms for Automated Calibration of Transcutaneous Spinal Cord Stimulation to Facilitate Clinical Applications

Transcutaneous spinal cord stimulation (tSCS) is a promising intervention that can benefit spasticity control and augment voluntary movement in spinal cord injury (SCI) and multiple sclerosis. Current applications require expert knowledge and rely on the thorough visual analysis of electromyographic (EMG) responses from lower-limb muscles to optimize attainable treatment effects. Here, we devised an automated tSCS setup by combining an electrode array placed over low-thoracic to mid-lumbar vertebrae, synchronized EMG recordings, and a self-operating stimulation protocol to systematically test various stimulation sites and amplitudes. A built-in calibration procedure classifies the evoked responses as reflexes or direct motor responses and identifies stimulation thresholds as recommendations for tSCS therapy. We tested our setup in 15 individuals (five neurologically intact, five SCI, and five Parkinson’s disease) and validated the results against blinded ratings from two clinical experts. Congruent results were obtained in 13 cases for electrode positions and in eight for tSCS amplitudes, with deviations of a maximum of one position and 5 to 10 mA in amplitude in the remaining cases. Despite these minor deviations, the calibration found clinically suitable tSCS settings in 13 individuals. In the remaining two cases, the automatic setup and both experts agreed that no reflex responses could be detected. The presented technological developments may facilitate the dissemination of tSCS into non-academic environments and broaden its use for diagnostic and therapeutic purposes