Background/Objectives: “Hermes” is an ankle–foot orthosis (AFO) with negative stiffness designed to mechanically compensate the symptomatic increase in plantarflexion (PF) torque (i.e., ankle joint torque resistance to dorsiflexion, DF) in patients with spastic paresis. Methods: The effectiveness of “Hermes” was evaluated in twelve patients with chronic unilateral spastic paresis after stroke. Using a robotic ankle manipulator, stiffness at the ankle joint was assessed across three conditions: ankle without Hermes (A), ankle with Hermes applying no torque compensation (A+H0%), and ankle with Hermes tuned to compensate 100% of the patients’ ankle joint stiffness (A+H100%). Results: A significant reduction in PF torque was found with Hermes applying compensation (A+H100%) compared to the conditions without Hermes (A) and with Hermes applying no compensation (A+H0%). Furthermore, a significant reduction in positive dorsiflexion work was found with Hermes applying compensation (A+H100%) compared to the condition with Hermes applying no compensation (A+H0%). Hermes did not significantly contribute to additional PF torque or positive work when applying no compensation (A+H0%). Conclusions: The reductions in PF torque achieved with Hermes are comparable to those seen with repeated ankle stretching programs and ankle robot training. Thus, Hermes is expected to assist voluntary dorsiflexion and improve walking in patients with spastic paresis.

To increase the quality of life of stroke patients, better diagnostics with the ability to identify the cause of motor impairment are needed. Robotic diagnostics increases the resolution of measurements, allows for tracking progress over a longer period, and can be used to evaluate new treatments. The Shoulder Elbow Perturbator (SEP) was developed to improve the diagnostics of post-stroke motor impairment. The SEP has already been tested on patients, showing promising results in identifying the cause of motor impairment, but no SEP system performance analysis has been published. To identify the joint properties of the elbow accurately, the SEP should have a bandwidth of at least 12 Hz. Furthermore, admittance and velocity control are required for various possible experimental tasks. This paper shows that the SEP performs adequately for the desired perturbations and experimental conditions for system identification of the human elbow. The SEP's performance is analysed with multisine signals to determine the bandwidth and endpoint dynamics. The velocity controller bandwidth is 50 Hz, and the admittance controller bandwidth is 65 Hz. Furthermore, the controller is stable. Thus, the SEP meets all the requirements and should be able to provide the desired perturbations and experimental conditions needed for system identification of the human elbow.

Amyotrophic lateral sclerosis (ALS) is a lethal progressive neurodegenerative disease characterized by upper motor neuron (UMN) and lower motor neuron (LMN) involvement. Their varying degree of involvement results in a clinical heterogenous picture, making clinical assessments of UMN signs in patients with ALS often challenging. We therefore explored whether instrumented assessment using robotic manipulation could potentially be a valuable tool to study signs of UMN involvement. We examined the dynamics of the wrist joint of 15 patients with ALS and 15 healthy controls using a Wristalyzer single-axis robotic manipulator and electromyography (EMG) recordings in the flexor and extensor muscles in the forearm. Multi-sinusoidal torque perturbations were applied, during which participants were asked to either relax, comply or resist. A neuromuscular model was used to study muscle viscoelasticity, e.g. stiffness (k) and viscosity (b), and reflexive properties, such as velocity, position and force feedback gains (kv, kp and kf, respectively) that dominated the responses. We further obtained clinical signs of LMN (muscle strength) and UMN (e.g. reflexes, spasticity) dysfunction, and evaluated their relation with the estimated neuromuscular model parameters. Only force feedback gains (kf) were elevated in patients (p = 0.033) compared to controls. Higher kf, as well as the resulting reflexive torque (Tref), were both associated with more severe UMN dysfunction in the examined arm (p = 0.040 and p < 0.001). Patients with UMN symptoms in the examined arm had increased kf and Tref compared to controls (both p = 0.037). Neither of these measures was related to muscle strength, but muscle stiffness (k) was lower in weaker patients (p = 0.012). All these findings were obtained from the relaxed test. No differences were observed during the instructions comply and resist. This findings are proof-of-concept that instrumented assessment using robotic manipulation is a feasible technique in ALS, which may provide quantitative, operator-independent measures relating to UMN symptoms. Elevated force feedback gains, driving larger reflexive muscle torques, appear to be particularly indicative of clinically established levels of UMN dysfunction in the examined arm.

Forensic reconstruction and scenario evaluation are crucial in investigations of suspicious deaths related to falls from a height. In such cases, distinguishing between accidental falls, being pushed or jumping is an important but difficult task, since objective methods to do so are currently lacking. This paper explores the possibility of repurposing a passive rigid body model of a human from commercially available crash simulation software for forensic reconstruction and scenario evaluation of humans dropping from heights. To use this approach, a prerequisite is that the human body model can produce realistic movements compared to those of a real human, given similar environmental conditions. Therefore, this study assessed the validity of the commercially available Simcenter Madymo Pedestrian Model (MPM) for simulating human fall movements. Experimental kinematic and kinetic data was collected from nine participants, who dropped from a height in three different ways: passively tilting over, getting pushed, and jumping. Next, the performance of the MPM in reproducing the kinematics of the experimental falls was assessed by comparing the orientation of the body 0.3s after platform release. The results show that the MPM currently does not consistently reproduce the experimentally recorded falling movements across multiple falling conditions and outcome measures. The MPM must therefore be adapted if to be used for forensic reconstruction and scenario evaluation, for example by implementing active movement.

Human hands are complex biomechanical systems that allow for dexterous tasks with many degrees of freedom. Coordination of the fingers is essential for many activities of daily living and involves integrating sensory signals. During this sensory integration, the central nervous system deals with the uncertainty of sensory signals. When handling compliant objects, force and position are related. Interactions with stiff objects result in reduced position changes and increased force changes compared to compliant objects. Literature has shown sensory integration of force and position at the shoulder. Nevertheless, differences in sensory requirements between proximal and distal joints may lead to different proprioceptive representations, hence findings at proximal joints cannot be directly transferred to distal joints, such as the digits. Here, we investigate the sensory integration of force and position during pinching. A haptic manipulator rendered a virtual spring with adjustable stiffness between the index finger and the thumb. Participants had to blindly reproduce a force against the spring. In both visual reference trials and blind reproduction trials, the relation between pinch force and spring compression was constant. However, by covertly changing the spring characteristics in catch trials into an adjusted force-position relation, the participants' weighting of force and position could be revealed. In agreement with previous studies on the shoulder, participants relied more on force sense in trials with higher stiffness. This study demonstrated stiffness-dependent sensory integration of force and position feedback during pinching.

Individuals with an upper motor neuron syndrome, e.g., stroke survivors, may have a pathological increase of passive ankle stiffness due to spasticity, that impairs ankle function and activities such as walking. To improve mobility, walking aids such as ankle-foot orthoses and orthopaedic shoes are prescribed. However, these walking aids generally limit the range of motion (ROM) of the foot and may therewith negatively influence activities that require a larger ROM. Here we present a new ankle-foot orthosis "Hermes", and its first experimental results from four hemiparetic chronic stroke patients. Hermes was designed to facilitate active ankle dorsiflexion by mechanically compensating the passive ankle stiffness using a negative-stiffness mechanism. Four levels of the Hermes' stiffness compensation (0%, 35%, 70% and 100%) were applied to evaluate active ROM in a robotic ankle manipulator and to test walking feasibility on an instrumented treadmill, in a single session. The robotic tests showed that Hermes successfully compensated the ankle joint stiffness in all four patients and improved the active dorsiflexion ROM in three patients. Three patients were able to walk with Hermes at one or more Hermes' stiffness compensation levels and without reducing their preferred walking speeds compared to those with their own walking aids. Despite a small sample size, the results show that Hermes holds great promise to support voluntary ankle function and to benefit walking and daily activities.

Humans vary the stiffness in their joints depending on tasks and circumstances. For posture control a high joint stiffness is required to withstand perturbations, whereas for force control a low joint stiffness is required. To investigate how humans vary their joint stiffness precisely for moving an arm, a wearable device is needed that can generate small force perturbations at the wrist while measuring the resulting muscular reactions. The majority of the state-of-the-art devices either offer too little versatility or impede the free movement of the arm. Based on a 3-DoF spatial redundant 4-RUU parallel manipulator applied in an inverted way where the original base with actuators has become the moving platform and the original moving platform is attached to the wrist as a bracelet, a versatile, 0.175 kg lightweight, low impedance, and compact wearable device was developed that can generate perturbation forces in X-, Y-, and Z-direction. The design and a prototype of the device are presented with experimental tests showing controlled perturbations in the order of 4 N with frequencies up to 12 Hz.

Tele-impedance increases interaction performance between a robotic tool and unstructured/unpredictable environments during teleoperation. However, the existing tele-impedance interfaces have several ongoing issues, such as long calibration times and various obstructions for the human operator. In addition, they are all designed to be controlled by the operator's arms, which can cause difficulties when both arms are used, as in bi-manual teleoperation. To resolve these issues, we designed a novel foot-based tele-impedance control method inspired by the human limb stiffness ellipse modulation. The proposed mechanical interface design includes a disc and a foot pressure sensor that controls the orientation and size/shape of the stiffness ellipse, respectively. We evaluated the disc interface control method in an experimental study with 12 participants, who performed a complex drilling task in a virtual environment. The results show the ability of the operator to use the proposed interface in order to dynamically adapt to different phases of the task and changes in the environment. In addition, a comparison with low and high uniform impedance modes demonstrates a superior interaction performance of the proposed method.

Tremor is thought to be an effect of oscillatory activity within the sensorimotor network. To date, the underlying pathological brain networks are not fully understood. Disentangling tremor activity from voluntary motor output and sensorimotor feedback systems is challenging. To better understand the intrinsic sensorimotor fingerprint underlying tremor, we aimed to disentangle the sensorimotor system into driving (motor) and feedback/compensatory (sensory) neuronal involvement, and aimed to pinpoint tremor activity in essential tremor (ET) and tremor-dominant Parkinson's disease (PD) with a novel closed-loop approach. Eighteen ET patients, 14 tremor-dominant PD patients, and 18 healthy controls were included. An MR-compatible wrist manipulator was employed during functional MRI (fMRI) while muscle activity during (in)voluntary movements was concurrently recorded using electromyography (EMG). Tremor was quantified based on EMG and correlated to brain activity. Participants performed three tasks: an active wrist motor task, a passive wrist movement task, and rest (no wrist movement). The results in healthy controls proved that our experimental paradigm activated the expected motor and sensory networks separately using the active (motor) and passive (sensory) task. ET patients showed similar patterns of activation within the motor and sensory networks. PD patients had less activity during the active motor task in the cerebellum and basal ganglia compared to ET and healthy controls. EMG showed that in ET, tremor fluctuations correlated positively with activity in the inferior olive region, and that in PD tremor fluctuations correlated positively with cerebellar activity. Our novel approach with an MR-compatible wrist manipulator, allowed to investigate the involvement of the motor and sensory networks separately, and as such to better understand tremor pathophysiology. In ET sensorimotor network function did not differ from healthy controls. PD showed less motor-related activity. Focusing on tremor, our results indicate involvement of the inferior olive in ET tremor modulation, and cerebellar involvement in PD tremor modulation.

Skeletal muscles generate force, enabling movement through a series of fast electro-mechanical activations coordinated by the central nervous system. Understanding the underlying mechanism of such fast muscle dynamics is essential in neuromuscular diagnostics, rehabilitation medicine and sports biomechanics. The unique combination of electromyography (EMG) and ultrafast ultrasound imaging (UUI) provides valuable insights into both electrical and mechanical activity of muscle fibers simultaneously, the excitation-contraction (E-C) coupling. In this feasibility study we propose a novel non-invasive method to simultaneously track the propagation of both electrical and mechanical waves in muscles using high-density electromyography and ultrafast ultrasound imaging (5000 fps). Mechanical waves were extracted from the data through an axial tissue velocity estimator based on one-lag autocorrelation. The E-C coupling in electrically evoked twitch contractions of the Biceps Brachii in healthy participants could successfully be tracked. The excitation wave (i.e. action potential) had a velocity of 3.9±0.5ms−1 and the subsequent mechanical (i.e. contraction) wave had a velocity of 3.5±0.9ms−1. The experiment showed evidence that contracting sarcomeres that were already activated by the action potential (AP) pull on sarcomeres that were not yet reached by the AP, which was corroborated by simulated contractions of a newly developed multisegmental muscle fiber model, consisting of 500 sarcomeres in series. In conclusion, our method can track the electromechanical muscle dynamics with high spatio-temporal resolution. Ultimately, characterizing E-C coupling in patients with neuromuscular diseases (e.g. Duchenne or Becker muscular dystrophy) may assess contraction efficiency, monitor the progression of the disease, and determine the efficacy of new treatment options.

Accurate and swift tuning of joint impedance is crucial to perform movement and interaction with our environment. Time-varying system identification enables quantification of joint impedance during movement. Many methods have been developed over the years, each with their own mathematical approach and underlying assumptions. Yet, for the identification of joint impedance, a systematic comparison revealing each method's unique strengths and weaknesses, is lacking. Here, we propose a quantitative framework to compare these methods. The framework is used to review five time-varying system identification methods using both simulated data and experimental data. These methods included three time-domain methods: ensemble, short data segment, and basis impulse response function; and two frequency-domain methods: ensemble spectral, and kernel-based regression. In the simulation study, joint stiffness - the static component of impedance - was simulated as a square wave to mimic the most extreme case of time-varying behavior. The identification results were compared based on the (1) variance accounted for (VAF), (2) bias, (3) random, and (4) total estimation error with respect to the simulated joint stiffness; and (5) rise time between two stiffness levels. In the experimental study, human ankle joint impedance was identified. Identification performance was compared using the variability in estimating joint stiffness - representative of the random error - and VAF. The performance metrics revealed distinct identification properties for each method. Therefore, researchers must make a well-justified decision which method is most appropriate for their application. The combination of simulation and experimental work with extensive performance quantification creates a framework for quantitative assessment of newly developed time-varying system identification methods.

Careful control of joint impedance, or dynamic joint stiffness, is crucial for successful performance of movement. Time-varying system identification (TV-SysID) enables quantification of joint impedance during movement. Several TV-SysID methods exist, but have never been systematically compared. Here, we simulate time-varying joint behavior and propose three performance metrics that enable to quantify and compare TV-SysID methods. Time-varying joint stiffness is simulated using a square wave and subsequently estimated with three TV-SysID methods: the ensemble, short data segment, and basis impulse response function method. These methods were compared based on (1) bias with respect to the simulated joint stiffness, (2) random error across 100 simulation trials, and (3) maximum adaptation speed in joint stiffness that can be captured. This approach revealed that each TV-SysID method has its own unique properties. The simulation method and performance metrics pave the way for developing a framework to quantify the strengths and weaknesses of TV-SysID algorithms for estimating joint impedance.

Objective: A role of the motor cortex in tremor generation in essential tremor (ET) is assumed, yet the directionality of corticomuscular coupling is unknown. Our aim is to clarify the role of the motor cortex. To this end we also study 'familial cortical myoclonic tremor with epilepsy' (FCMTE) and slow repetitive voluntary movements with a known cortical drive. Methods: Directionality of corticomuscular coupling (EEG-EMG) was studied with renormalized partial directed coherence (rPDC) during tremor in 25 ET patients, 25 healthy controls (mimicked) and in seven FCMTE patients; and during a self-paced 2 Hz task in eight ET patients and seven healthy controls. Results: Efferent coupling around tremor frequency was seen in 33% of ET patients, 45.5% of healthy controls, all FCMTE patients, and, around 2 Hz, in all ET patients and all healthy controls. Ascending coupling, seen in the majority of all participants, was weaker in ET than in healthy controls around 5-6 Hz. Conclusions: Possible explanations are that tremor in ET results from faulty subcortical output bypassing the motor cortex; rate-dependent transmission similar to generation of rhythmic movements; and/or faulty feedforward mechanism resulting from decreased afferent (sensory) coupling. Significance: A linear cortical drive is lacking in the majority of ET patients. (C) 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.

Motion capture systems are extensively used to track human movement to study healthy and pathological movements, allowing for objective diagnosis and effective therapy of conditions that affect our motor system. Current motion capture systems typically require marker placements which is cumbersome and can lead to contrived movements. Here, we describe and evaluate our developed markerless and modular multi-camera motion capture system to record human movements in 3D. The system consists of several interconnected single-board microcomputers, each coupled to a camera (i.e., the camera modules), and one additional microcomputer, which acts as the controller. The system allows for integration with upcoming machine-learning techniques, such as DeepLabCut and AniPose. These tools convert the video frames into virtual marker trajectories and provide input for further biomechanical analysis. The system obtains a frame rate of 40 Hz with a submilli-second synchronization between the camera modules. We evaluated the system by recording index finger movement using six camera modules. The recordings were converted via trajectories of the bony segments into finger joint angles. The retrieved finger joint angles were compared to a marker-based system resulting in a root-mean-square error of 7.5 degrees difference for a full range metacarpophalangeal joint motion. Our system allows for out-of-the-lab motion capture studies while eliminating the need for reflective markers. The setup is modular by design, enabling various configurations for both coarse and fine movement studies, allowing for machine learning integration to automatically label the data. Although we compared our system for a small movement, this method can also be extended to full-body experiments in larger volumes.

INTRODUCTION:Sensorimotor control can be disturbed because of pain and trauma. There is scarce comprehension about which component of the sensorimotor system would benefit the most from treatment in distal radius fracture (DRF).PURPOSE OF THE STUDY:The purpose of this study was to determine whether the sensorimotor control of subjects with a history of DRF impaired compared with healthy subjects. If so, which component of the sensorimotor system is most affected.METHODS:Nine healthy participants and 11 participants with a DRF history executed posture and reproduction tasks in interaction with a robotic wrist manipulator. A posture task with force perturbations assess sensorimotor control. Position and force reproduction tasks assessed sensory feedback. Electromyography recorded the muscle activity to study the motor part of the sensorimotor system.STUDY DESIGN:Cross-sectional case-control.RESULTS:The results showed that the motor responses to the perturbations during the posture task did not differ significantly, whereas the position reproduction did significantly differ between the 2 groups. Moreover, participants with a DRF history did not adapt to the changed dynamics of the environment during the posture task, whereas the controls did.DISCUSSION:The results of this study imply that processing of sensory position feedback is impaired in people with a DRF history while sensorimotor control during a posture task is unaffected. A possible explanation for these results is that different neural networks are involved during reproduction and posture tasks.CONCLUSIONS:A history of DRF is related to disturbed processing of sensory feedback of the sensorimotor system, especially the Joint Position Sense, which leads to an impairment in detecting a changed environment and adapting to it. Impaired Joint Position Sense and thereby the inability to adapt adequately to a changing environment should be taken into account during the rehabilitation of patients with DRF.

Background Parkinson’s disease (PD) and essential tremor (ET) are neurodegenerative diseases characterized by movement deficits. Especially in PD, maintaining cyclic movement can be significantly disturbed due to pathological changes in the basal ganglia and the cerebellum. Providing external cues improves timing of these movements in PD and also affects ET. The aim of this study is to determine differences in cortical activation patterns in PD and ET patients during externally and internally cued movements. Methods Eleven PD patients, twelve ET patients, OFF tremor suppressing medication, and nineteen age-matched healthy controls (HC) were included and asked to perform a bimanual tapping task at two predefined cue frequencies. The auditory cue, a metronome sound presented at 2 or 4 Hz, was alternately switched on and off every 30 s. Tapping at two different frequencies were used since it is expected that different brain networks are involved at different frequencies as has been shown in previous studies. Cortical activity was recorded using a 64-channel EEG cap. To establish the cortical activation pattern in each group, the task related power (TRP) was calculated for each subject. For inter-groups analysis, EEG electrodes for divided into 5 different areas. Results Inter-group analysis revealed significant differences in areas responsible for motor planning, organization and regulation and involved in initiation, maintenance, coordination and planning of complex sequences of movements. Within the area of the primary motor cortex the ET group showed a significantly lower TRP than the HC group. In the area responsible for combining somatosensory, auditory and visual information both patient groups had a higher TRP than the HC group. Conclusions Different neurological networks are involved during cued and non-cued movements in ET, PD and HC. Distinct cortical activation patterns were revealed using task related power calculations. Different activation patterns were revealed during the 2 and 4 Hz tapping task indicating different strategies to execute movements at these rates. The results suggest that a including a cued/non-cued tapping task during clinical decision making could be a valuable tool in an objective diagnostic protocol.

During movements, humans continuously regulate their joint impedance to minimize control effort and optimize performance. Joint impedance describes the relationship between a joint's position and torque acting around the joint. Joint impedance varies with joint angle and muscle activation and differs from trial-to-trial due to inherent variability in the human control system. In this paper, a dedicated time-varying system identification (SI) framework is developed involving a parametric, kernel-based regression, and nonparametric, "skirt decomposition," SI method to monitor the time-varying joint impedance during a force task. Identification was performed on single trials and the estimators included little a priori assumptions regarding the underlying time-varying joint mechanics. During the experiments, six (human) participants used flexion of the wrist to apply a slow sinusoidal torque to the handle of a robotic manipulator, while receiving small position perturbations. Both methods revealed that the sinusoidal change in joint torque by activation of the wrist flexor muscles resulted in a sinusoidal time-varying joint stiffness and resonance frequency. A third-order differential equation allowed the parametric kernel-based estimator to explain on average 76% of the variance (range 52%-90%). The nonparametric skirt decomposition method could explain on average 84% of the variance (range 66%-91%). This paper presents a novel framework for identification of time-varying joint impedance by making use of linear time-varying models based on a single trial of data.

BackgroundThe basal ganglia and cerebellum are brain structures involved in movement initiation, execution and termination. They are thought to be involved in the tremor generation and movement deficits in Parkinson's disease (PD) and essential tremor (ET). Especially in PD, maintaining cyclic movement, such as walking or tapping can be significantly disturbed. Providing external cues improves timing of these movements in PD but its effect on ET has not yet been studied in depth. The aim of this study is to evaluate the usefulness of a bimanual tapping task as a tool during clinical decision making.MethodHand movements and tremor was recorded using accelerometers and EMG (m. extensor carpi ulnaris) from PD and ET patients and healthy controls during a bimanual tapping task as a way to distinguish PD from ET. All subjects performed the tapping task at two different frequencies, 2Hz and 4 Hz, with and without the presence of auditory cues.ResultsNo significant intra-group differences were found in the patient groups. Acceleration data revealed significantly less accurate tapping and more variable tapping in PD than in ET and healthy controls. ET subjects tapped less accurate and with a greater variability than healthy controls during the 4Hz tapping task. Most interestingly the tapping accuracy improved in PD patients when kinetic tremor was recorded with EMG during the task.ConclusionProviding ET and PD patients with an external cue results in different tapping performances between patient groups and healthy controls. Furthermore, the findings suggest that kinetic tremor in PD enables patients to perform the task with a greater accuracy. So far this has not been shown in other studies.

There is no objective gold standard to detect tremors. This concerns not only the choice of the algorithm and sensors, but methods are often designed to detect tremors in one specific group of patients during the performance of a specific task. Therefore, the aim of this study is twofold. First, an objective quantitative method to detect tremor windows (TWs) in accelerometer and electromyography recordings is introduced. Second, the tremor stability index (TSI) is determined to indicate the advantage of detecting TWs prior to analysis. Ten Parkinson’s disease (PD) patients, ten essential tremor (ET) patients, and ten healthy controls (HC) performed a resting, postural and movement task. Data was split into 3-s windows, and the power spectral density was calculated for each window. The relative power around the peak frequency with respect to the power in the tremor band was used to classify the windows as either tremor or non-tremor. The method yielded a specificity of 96.45%, sensitivity of 84.84%, and accuracy of 90.80% of tremor detection. During tremors, significant differences were found between groups in all three parameters. The results suggest that the introduced method could be used to determine under which conditions and to which extent undiagnosed patients exhibit tremors.