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Introduction: Children with cerebral palsy (CCP) benefit from intensive arm training. Exergames that can be played at home offer the possibility to increase the frequency of therapy but require reliable and accurate real-time motion tracking via easy-to-use sensors in unsupervised settings and magnetically disturbed environments. Method: We propose an inertial-sensor-based method with a single sensor on the wrist for real-time tracking of the inclination of the forearm. The control parameter of the game was validated with an optical marker-based ground truth system. Results: First experiments with a therapist performing training movements in a healthy and simulated spastic manner show that the forearm inclination well captures the motion dynamics. The accuracy of the inertial-sensor-based measurement is validated with respect to the reference system in three healthy subjects. Orientation offsets between the inertial sensor and the forearm marker set in the range of 2° ̶ 6° and dynamic measurement errors about 3.1° were obtained. Conclusion: This work demonstrates that the proposed method is suitable for real-time control of exergames of CCP. The validation with an optical reference system showed that the forearm inclination can be used as for feedback and therapeutic progress monitoring.

A sensor setup with a small number of sensors, i.e., a sparse sensor setup, could enable a comprehensive unobtrusive motion analysis using inertial motion capture (IMC) with little obtrusion to the user, which is critical for clinical and sports applications, such as assessing disease, providing biofeedback in rehabilitation, or enhancing performance in sports. When sparse sensor setups were used, the motion analysis was either not comprehensive or physical correctness was not considered. Furthermore, the relationship between sensor setup and the resulting accuracy of spatiotemporal, kinematic, or kinetic variables has not been investigated systematically. Therefore, we investigated the accuracy of biomechanical gait reconstructions from different sparse inertial sensor setups, in which the number of sensors is reduced by not placing sensors on all body segments of interest. We created biomechanical reconstructions by solving optimal control problems with a sagittal plane musculoskeletal model. The resulting simulations tracked raw accelerometer and gyroscope data while minimizing muscular effort. We applied this approach to six different sparse sensor setups to investigate the reconstruction quality of spatiotemporal, kinematic, and kinetic variables for each of these setups at three walking and three running speeds. We found that correlations between IMC and optical motion capture (OMC) were large for all sensor setups, but that those setups without a pelvis sensor led to a forward trunk lean that was too high, as well as unrealistic kinetics and kinematics. Spatiotemporal variable estimations generally also benefitted from a pelvis sensor, but not as much as the kinetic and kinematic estimations. We found that for setups including a pelvis sensor, there was no clear benefit for including additional sensors. While walking speed, step length, and joint kinematics generally benefited from using more sensors, the hip moment and knee moment did not. During running, the ankle moment and vertical ground reaction force were even estimated more accurately with smaller sensor setups. Therefore, we conclude that sagittal-plane reconstruction of walking and running kinetics and kinematics using sparse sensor setups can improve usability at the cost of only minor reductions in accuracy. When using a sparse sensor setup for gait reconstructions using optimal control, we advise to always include a pelvis sensor to achieve accurate results.

Multi-agent systems (MASs) can autonomously learn to solve previously unknown tasks by means of each agent's individual intelligence as well as by collaborating and exploiting collective intelligence. This article considers a group of autonomous agents learning to track the same given reference trajectory in a possibly small number of trials. We propose a novel collective learning control method that combines iterative learning control (ILC) with a collective update strategy. We derive conditions for desirable convergence properties of such systems. We show that the proposed method allows the collective to combine the advantages of the agents' individual learning strategies and thereby overcomes trade-offs and limitations of single-agent ILC. This benefit is achieved by designing a heterogeneous collective, i.e., a different learning law is assigned to each agent. All theoretical results are confirmed in simulations and experiments with two-wheeled-inverted-pendulum robots (TWIPRs) that jointly learn to perform the desired maneuver.

During the active visual exploration of the world rapid eye movements, so-called saccades, shift the visual image across the human retina. Although these high-speed shifts introduce considerable amounts of intra-saccadic motion smear, our perceptual experience is oblivious to it – a phenomenon termed saccadic omission. Yet, remaining unaware of saccade-induced motion smear during natural vision does not entail that it cannot be perceived in principle. Here we triggered tachistoscopic displays of natural scenes upon the onset of saccades to render smear, that would otherwise be omitted from perception, highly conspicuous. By systematically manipulating display durations we studied the dynamics of smear in a time-resolved manner, assessing identification performance of smeared scenes, as well as the perceived smear amount and direction. Both measures showed distinctive, U-shaped time courses throughout the saccade, indicating that not only generation but also reduction of perceived smear occurred during saccades. The spectral content of the scene strongly modulated these time courses, as did presenting only parts of the scene of varying sizes. Using reverse-regression analyses we further identified low spatial frequencies and orientations parallel to the direction of the ongoing saccade as the predominant visual features encoded in motion smear. We show that this finding can be explained by early visual models that assume no more than saccadic velocity and human contrast sensitivity profiles. Finally, we present a motion-filter model capable of predicting observers’ perceived amount of smear based on their eyes’ trajectories, suggesting a direct link between perceptual and saccade dynamics. Saccadic omission of motion smear may be conceptualized as a parsimonious visual mechanism that emerges naturally as a consequence of the interplay of the retinal consequences of saccades and early visual processing.

Hypotensive periods during surgery lead to an increase in postoperative complications. To further avoid the adverse events associated with the invasive measurement of the blood pressure, a perioperative, continuous, non-invasive blood pressure measurement method was developed. The precision of the method was tested for different mean pressure values and different thicknesses of the tissue model using a specifically designed and validated simulator that has the characteristics of the lower arm. The mean pressure difference between the pressure determined by the proposed method and simulated blood pressure from the simulator was 3.93 mmHg (±4.75 mmHg) for the systole and 4.89 mmHg (±6.10 mmHg) for the diastole. The results showed that the method can be a promising alternative to invasive blood pressure measurement methods.

Gestural interaction with freehands and while grasping an everyday object enables always-available input . To sense such gestures, minimal instrumentation of the user’s hand is desirable. However, the choice of an effective but minimal IMU layout remains challenging, due to the complexity of the multi-factorial space that comprises diverse finger gestures, objects and grasps. We present SparseIMU , a rapid method for selecting minimal inertial sensor-based layouts for effective gesture recognition. Furthermore, we contribute a computational tool to guide designers with optimal sensor placement. Our approach builds on an extensive microgestures dataset that we collected with a dense network of 17 inertial measurement units (IMUs). We performed a series of analyses, including an evaluation of the entire combinatorial space for freehand and grasping microgestures (393K layouts), and quantified the performance across different layout choices, revealing new gesture detection opportunities with IMUs. Finally, we demonstrate the versatility of our method with four scenarios.

Here, we present initial results on an inertial sensor controlled neuroprosthetic system that supports people with spinal cord injuries during swimming exercises. This novel electrical stimulation system allows paraplegics to perform swimming exercises using their own legs.

This paper presents the proof-of-concept of a home-based gamified wrist rehabilitation training system for children with cerebral palsy (CCP). We describe the user-centered design process of this system, which is composed of a wrist-worn inertial measurement unit (IMU) and a tangible device with an embedded IMU. The system employs a quaternion-based algorithm for automatic real-time estimation of the range of motion (RoM) covered by adduction/abduction and flexion/extension motions of the wrist. Experimental validation shows that the RoM can be determined with sufficient accuracy to control a game and that the algorithm is applicable in CCP. A serious game, which uses the presented algorithm and enables feedback as well as motivating stimuli, is implemented and evaluated by physiotherapists.

Human motion analysis using inertial measurement units (IMUs) has recently been shown to provide accuracy similar to the gold standard, optical motion capture, but at lower costs and while being less restrictive and time-consuming. However, IMU-based motion analysis requires precise knowledge of the orientations in which the sensors are attached to the body segments. This knowledge is commonly obtained via time-consuming and error-prone anatomical calibration based on precisely defined poses or motions. In the present work, we propose a self-calibrating approach for magnetometer-free joint angle tracking that is suitable for joints with two degrees of freedom (DoF), such as the elbow, ankle, and metacarpophalangeal finger joints. The proposed methods exploit kinematic constraints in the angular rates and the relative orientations to simultaneously identify the joint axes and the heading offset. The experimental evaluation shows that the proposed methods are able to estimate plausible and consistent joint axes from just ten seconds of arbitrary elbow joint motion. Comparison with optical motion capture shows that the proposed methods yield joint angles with similar accuracy as a conventional IMU-based method while being much less restrictive. Therefore, the proposed methods improve the practical usability of IMU-based motion tracking in many clinical and biomedical applications.

This work addresses the problem of reference tracking in autonomously learning robots with unknown, nonlinear dynamics. Existing solutions require model information or extensive parameter tuning, and have rarely been validated in real-world experiments. We propose a learning control scheme that learns to approximate the unknown dynamics by a Gaussian Process (GP), which is used to optimize and apply a feedforward control input on each trial. Unlike existing approaches, the proposed method neither requires knowledge of the system states and their dynamics nor knowledge of an effective feedback control structure. All algorithm parameters are chosen automatically, i.e. the learning method works plug and play. The proposed method is validated in extensive simulations and real-world experiments. In contrast to most existing work, we study learning dynamics for more than one motion task as well as the robustness of performance across a large range of learning parameters. The method’s plug and play applicability is demonstrated by experiments with a balancing robot, in which the proposed method rapidly learns to track the desired output. Due to its model-agnostic and plug and play properties, the proposed method is expected to have high potential for application to a large class of reference tracking problems in systems with unknown, nonlinear dynamics.

In motion tracking of connected multi-body systems Inertial Measurement Units (IMUs) are used in a wide variety of applications, since they provide a low-cost easy-to-use method for orientation estimation. However, in indoor environments or near ferromagnetic material the magnetic field is inhomogeneous which limits the accuracy of tracking algorithms using magnetometers. Methods that use only accelerometers and gyroscopes on the other hand yield no information on the absolute heading of the tracked object. For objects connected by rotational joints with range of motion constraints we propose a method that provides a magnetometer-free, long-term stable relative orientation estimate based on a non-linear, window-based cost function. The method can be used for real-time estimation as well as post-processing. It is validated experimentally with a mechanical joint and compared to other methods that are used in motion tracking. It is shown that for the used test object, the proposed methods yields the best results with a total angle error of less than 4 degrees for all experiments., Comment: IFAC WC 2020 Submission

Freezing of Gait (FoG) is one of the cardinal symptoms of Parkinson’s disease, which arises in the late stages of the disease. It affects the gait cycle and increases the risk of falling. FoG leads to heterogeneous gait cycles, which makes the detection of gait phases and events difficult. In this article, we introduce a new inertial measurement unit-based approach for detecting Parkinsonian gait phases based on the acceleration, velocity, rate of turn and orientation of the foot. Furthermore, we introduce a new gait evaluation measurement, the so-called GaitScore, for distinguishing between normal and FoG-affected motion phases and thus for detecting FoG episodes. Preliminary results show that the extreme values of the pitch angle during a motion phase provide valuable information for the detection of FoG. The proposed method can detect FoG episodes with a sensitivity of 97% and specificity of 87%. The reference data were generated by clinical experts who annotated FoG episodes in video data synchronized with the measurements of the inertial sensors. The detection of FoG in real-time enables on-demand cueing.

Power systems are subject to fundamental changes due to the increasing infeed of renewable energy sources. Taking the accompanying decentralization of power generation into account, the concept of prosumer-based microgrids gives the opportunity to rethink structuring and operation of power systems from scratch. In a prosumer-based microgrid, each power grid node can feed energy into the grid and draw energy from the grid. The concept allows for spatial aggregation such that also an interaction between microgrids can be represented as a prosumer-based microgrid. The contribution of this work is threefold: (i) we propose a decentralized hierarchical control approach in a network including different time scales, (ii) we use iterative learning control to compensate periodic demand patterns and save lower layer control energy and (iii) we assure asymptotic stability and monotonic convergence in the iteration domain for the linearized dynamics and validate the performance by simulating the nonlinear dynamics., accepted for IFAC World Congress 2020

This work addresses the problem of reference tracking in autonomously learning agents with unknown, nonlinear dynamics. Existing solutions require model information or extensive parameter tuning, and have rarely been validated in real-world experiments. We propose a learning control scheme that learns to approximate the unknown dynamics by a Gaussian Process (GP), which is used to optimize and apply a feedforward control input on each trial. Unlike existing approaches, the proposed method neither requires knowledge of the system states and their dynamics nor knowledge of an effective feedback control structure. All algorithm parameters are chosen automatically, i.e. the learning method works plug and play. The proposed method is validated in extensive simulations and real-world experiments. In contrast to most existing work, we study learning dynamics for more than one motion task as well as the robustness of performance across a large range of learning parameters. The method’s plug and play applicability is demonstrated by experiments with a balancing robot, in which the proposed method rapidly learns to track the desired output. Due to its model-agnostic and plug and play properties, the proposed method is expected to have high potential for application to a large class of reference tracking problems in systems with unknown, nonlinear dynamics.

Real-time motion tracking of kinematic chains is a key prerequisite in the control of, e.g., robotic actuators and autonomous vehicles and also has numerous biomechanical applications. In recent years, it has been shown that, by placing inertial sensors on segments that are connected by rotational joints, the motion of that kinematic chain can be tracked accurately. These methods specifically avoid using magnetometer measurements, which are known to be unreliable since the magnetic field at the different sensor locations is typically different. They rely on the assumption that the motion of the kinematic chain is sufficiently rich to assure observability of the relative pose. However, a formal investigation of this crucial requirement has not yet been presented, and no specific conditions for observability have so far been given. In this work, we present an observability analysis and show that the relative pose of the body segments is indeed observable under a very mild condition on the motion. We support our results by simulation studies, in which we employ a state estimator that neither uses magnetometer measurements nor additional sensors and does not impose assumptions on the accelerometer to measure only the direction of gravity, nor on the range of motion or degrees of freedom of the joints. We investigate the effect of the amount of excitation and of stationary periods in the data on the accuracy of the estimates. We then use experimental data from two mechanical joints as well as from a human gait experiment to validate the observability criterion in practice and to show that small excitation levels are sufficient for obtaining accurate estimates even in the presence of time periods during which the motion is not observable., Comment: 17 pages, 8 figures

In this contribution a motion estimation approach for the autonomous flight of tethered airfoils is presented. Accurate motion data are essential for the airborne wind energy sector to optimize the harvested wind energy and for the manufacturer of tethered airfoils to optimize the kite design based on measurement data. We propose an estimation based on tether angle measurements from the ground unit and inertial sensor data from the airfoil. In contrast to existing approaches, we account for the issue of tether sag, which renders tether angle measurements temporarily inaccurate. We formulate a Kalman Filter which adaptively shifts the fusion weight to the measurement with the higher certainty. The proposed estimation method is evaluated in simulations, and a proof of concept is given on experimental data, for which the proposed method yields a three times smaller estimation error than a fixed-weight solution.

Hand motion tracking is a key technology in several applications including ergonomic workplace assessment, human-machine interaction and neurological rehabilitation. Recent technological solutions are based on inertial measurement units (IMUs). They are less obtrusive than exoskeleton-based solutions and overcome the line-of-sight restrictions of optical systems. The number of sensors is crucial for usability, unobtrusiveness, and hardware cost. In this paper, we present a real-time capable, sparse motion tracking solution for hand motion tracking that requires only five IMUs, one on each of the distal finger segments and one on the back of the hand, in contrast to recently proposed full-setup solution with 16 IMUs. The method only uses gyroscope and accelerometer readings and avoids magnetometer readings, which enables unrestricted use in indoor environments, near ferromagnetic materials and electronic devices. We use a moving horizon estimation (MHE) approach that exploits kinematic constraints to track motions and performs long-term stable heading estimation. The proposed method is validated experimentally using a recently developed sensor system. It is found that the proposed method yields qualitatively good agreement of the estimated and the actual hand motion and that the estimates are long-term stable. The root-mean-square deviation between the fingertip position estimates of the sparse and the full setup are found to be in the range of 1 cm. The method is hence highly suitable for unobtrusive and non-restrictive motion tracking in a range of applications.

Functional electrical stimulation (FES) is used to support gait in stroke patients and to induce cycling motions in paralyzed legs. In the current contribution, we present a method that, for the first time, enables FES-supported swimming in paraplegics. The proposed setup includes a waterproof stimulator, cables, and electrodes. In preliminary experiments, flexion and extension movements of the knee were generated in a completely paralyzed subject to support the propulsion. Furthermore, transcutaneous spinal cord stimulation (tSCS) is used to get a straight swimming position and to reduce spasticity of the lower extremities. The developed setting remained dry and safe during all sessions. The first trials revealed the need for a synchronization of the patient’s arm movements with the artificially induced leg movements to prevent undesired rolling movements of the swimmer. To enable such a synchronized swimming, a trigger algorithm was developed that is based on the roll angle and angular velocity of the trunk. By experimental validation, it was demonstrated that a functional stimulation pattern can be generated during front crawl movements of the upper body. The new setup and methods are currently being tested during the STIMSWIM pilot study with paraplegics. The preliminary results of the first two subjects show an improvement of the swimming speed of approximately 15% for FES/tSCS assisted swimming compared to non-assisted swimming and a clear training effect over the first 7 sessions.

Inertial Measurement Units (IMUs) are increasingly used for human motion analysis. However, two major challenges remain: First, one must know precisely in which orientation the sensor is attached to the respective body segment. This is commonly achieved by accurate manual placement of the sensors or by letting the subject perform tedious calibration movements. Second, standard methods for inertial motion analysis rely on a homogeneous magnetic field, which is rarely found in indoor environments. To address both challenges, we introduce an automatic calibration method for joints with two degrees of freedom such as the combined radioulnar and elbow joint. While the user performs arbitrary movements, the method automatically identifies the sensor-to-segment orientations by exploiting the kinematic constraints of the joint. Simultaneously, the method identifies and compensates the influence of magnetic disturbances on the sensor orientation quaternions and the joint angles. In experimental trials, we obtain angles that agree well with reference values from optical motion capture. We conclude that the proposed method overcomes mounting and calibration restrictions and improves measurement accuracy in indoor environments. It therefore improves the practical usability of IMUs for many medical applications.

Inertial measurement units (IMUs) are a promising tool for realtime analysis of human motion in many application domains. We consider the standard sensor fusion problem in IMU orientation estimation. We highlight the malicious effects that inhomogeneous magnetic fields, which are often found in indoor environments, can have on the inclination portions (roll and pitch) of the orientation estimate. We then propose a novel method that uses an analytical solution of the sensor fusion problem and purely horizontal magnetometer-based corrections. The method assures that magnetic field measurements affect only the heading (yaw) component of the orientation estimate. Furthermore, we parametrize the algorithm such that the user can choose the time constant and aggressiveness with which the algorithm balances between gyroscope drift compensation and rejection of disturbances caused by inhomogeneous magnetic fields or by velocity changes.

Rehabilitation robotics and Functional Electrical Stimulation (FES) are becoming more important in the rehabilitation of stroke and spinal cord injured (SCI) patients. Patient-cooperative control strategies help to only compensate for deficits and to not support too much. The application of a cable-driven arm robotic system with constant force support is considered. FES of the biceps and triceps, as well as of the posterior and anterior part of the deltoid muscle allows us to control the flexion and extension of the elbow joint and of the shoulder motion in the transversal plane. In order to support these motions of the patient, we introduce a novel assist-as-needed FES support based on iterative learning vector fields. Using this new FES control scheme, we aim to facilitate the patient’s timing and completion of the motion. The approach is evaluated in experimental trials with healthy subjects performing a breaststroke swimming motion. Starting from a patient-typical deviation of 50°, the controller automatically adapts the support by adjusting the stimulation and thereby reduces the deviation to approximately 15° within less than ten strokes.

Functional Electrical Stimulation (FES) can be used to support the gait of stroke patients. By measuring joint angles and adjusting the stimulation intensities automatically to the current need of the patient, setup times can be reduced and time-variant effects like muscle fatigue can be compensated. This was achieved in recent publications by using Iterative Learning Control (ILC) on the ankle complex. In this paper we consider FES of the antagonistic knee muscle complex (quadriceps and hamstring muscles) that controls knee flexion/extension. We used a coactivation strategy in order to map the two stimulation channels to a single control input. A large class of dynamic models was obtained by system identification based on data from two experiments: one with standing subjects and one with subjects walking on a treadmill while being stimulated during different time segments of the gait cycle. Time delays, system poles, and in particular the system gains were found to vary largely. Furthermore, large differences were observed between muscle dynamics in standing pose and during walking. We designed an iterative learning controller that is stable for almost all models. In experiments with eight healthy subjects walking on a treadmill, the ILC was found to reduce deviations from a reference trajectory to about five degrees within two strides.

The assessment of hand posture and kinematics is increasingly important in various fields. This includes the rehabilitation of stroke survivors with restricted hand function. This paper presents a modular, ambulatory measurement system for the assement of the remaining hand function and for closed-loop controlled therapy. The device is based on inertial sensors and utilizes up to five interchangeable sensor strips to achieve modularity and to simplify the sensor attachment. We introduce the modular hardware design and describe algorithms used to calculate the joint angles. Measurements with two experimental setups demonstrate the feasibility and the potential of such a tracking device.

In this paper a novel knee joint rehabilitation device made for controlled active motion (CAM) therapy is presented and tested. More precisely, the system is a redesign of an originally passive CAM device, called CAMOped. Instead of a break, the adjustable resistance, which is needed for CAM therapy, is now provided via a torque-controlled pneumatic-muscle-actuator-driven joint. These actuators are inherently compliant and can produce both a variable restistance and, by co-contraction, a variable stiffness. It will be shown that, by measuring the foot contact forces and using them as feedback information, the foot load can be adjusted very precisely up to 100 N. Furthermore, it will be demonstrated that, in contrast to passive systems, the presented active systems is capable of varying the resistance while the device is in use. This facilitates adapting the resistance to the patient’s needs in real time and to use joint-angle- or foot-position-dependent resistance curves.

Inertial measurement units are commonly used in a growing number of application fields to track or capture motions of kinematic chains, such as human limbs, exoskeletons or robotic actuators. A major challenge is the presence of magnetic disturbances that results in unreliable magnetometer readings. Recent research revealed that this problem can be overcome by exploitation of kinematic constraints. While typically each segment of the kinematic chain is equipped with an IMU, a novel approach called sparse inertial motion tracking aims at inferring the complete motion states from measurements of a reduced set of sensors. In the present contribution, we combine the magnetometer-free and the sparse approach for real-time motion tracking of double-hinge joint systems with non-parallel joint axes. Analyzing the observability of the system, we find a condition which assures that the relative orientations between all segments are uniquely determined by a kinematic constraint, which contains only the gyroscope readings. Furthermore, we propose a moving-horizon estimator and validate it in a simulation study of four movements, which differ by their degrees of excitation. The results of this study confirm the theoretical conjectures and demonstrate that magnetometer-free sparse inertial real-time motion tracking is feasible under precise and simple excitation conditions.

Inertial measurement units are commonly used to estimate the attitude of moving objects. Numerous nonlinear filter approaches have been proposed for solving the inherent sensor fusion problem. However, when a large range of different dynamic and static rotational and translational motions is considered, the attainable accuracy is limited by the need for situation-dependent adjustment of accelerometer and gyroscope fusion weights. We investigate to what extent these limitations can be overcome by means of artificial neural networks and how much domain-specific optimization of the neural network model is required to outperform the conventional filter solution. A diverse set of motion recordings with a marker-based optical ground truth is used for performance evaluation and comparison. The proposed neural networks are found to outperform the conventional filter across all motions only if domain-specific optimizations are introduced. We conclude that they are a promising tool for inertial-sensor-based real-time attitude estimation, but both expert knowledge and rich datasets are required to achieve top performance., Comment: accepted for the 23rd International Conference on Information Fusion

This work presents a system to benchmark humanoid posture control and balance performances under perturbed conditions. The specific benchmarking scenario consists, for example, of balancing upright stance while performing voluntary movements on moving surfaces. The system includes a motion platform used to provide the perturbation, an innovative body-tracking system suitable for robots, humans and exoskeletons, control software and a set of predefined perturbations, a humanoid robot used to test algorithms, and analysis software providing state of the art data analysis used to provide quantitative measures of performance. In order to provide versatility, the design of the system is oriented to modularity: all its components can be replaced or extended according to experimental needs, adding additional perturbation profiles, new evaluation principles, and alternative tracking systems. It will be possible to use the system with different kinds of robots and exoskeletons as well as for human experiments aimed at gaining insights into human balance capabilities.

In industrial robotics, controller parameters for force control must be adjusted to the specific robot that performs a task and they must be re-adjusted when the same task is to be performed by another robot. We address this challenge by proposing a transferable force controller for contact establishment between robot and surface. The controller is implemented based on task frame formalism. The proposed controller is based on prescribed performance control (PPC) and does not rely on a dynamic model of the environment. Due to the inherent robustness of PPC, it can be used to ensure similar performance for the same task across different robots and environments. The proposed controller is validated experimentally in a simple contact establishment task performed by three different robots (Universal Robots UR5, Franka Emika Panda, Denso Wave VS087) and three different board materials providing different stiffness (steel, aluminum, PVC). The PPC is found to yield an up to two orders of magnitude smaller variance of closed-loop settling time across all robots and materials than a conventional impedance controller.

Recent research indicates that music-supported therapies may offer unique benefits for rehabilitation of motor function after stroke. We designed an adapted guitar and training task aimed to improve coordination between rhythmic and discrete movements because individuals recovering from stroke have greater difficulty performing discrete vs. rhythmic movements. In this paper, we report a feasibility study on training to play this adapted guitar in healthy young adults. Subjects (N = 10) practiced two rhythmic strumming patterns over three consecutive days using their non-dominant hand guided by an audiovisual metronome (60 bpm). They were also instructed to press a foot pedal while maintaining the strumming movement. Elbow and wrist kinematics were estimated using wireless inertial measurement units. Results showed positive mean asynchrony between strum onsets and metronome onsets, and a decrease in the standard deviation of mean asynchrony over practice. In early practice, participants slowed the strumming movement when they pressed the foot pedal, but this interference decreased on days two and three. Smoothness of the elbow movement during the strum phase, which includes the contact with the strings, increased over practice, while smoothness of the return phase did not change over practice. The predominant joint coordination pattern used for the strum phase consisted of elbow extension coupled with elbow pronation, wrist extension, and ulnar deviation. We discuss how these results fit into current music-based rehabilitation literature and outline directions for future applications of this music-supported intervention.

Inertial Measurement Units (IMUs) are used to track the motion of kinematic chains in a wide variety of robotic and biomedical applications. However, inertial motion tracking is severely limited by the fact that magnetic fields are inhomogeneous in indoor environments and near electronic devices. Methods that use only accelerations and angular rates for orientation estimation yield no absolute heading information and suffer from heading drift. To overcome this limitation, we propose a novel method that exploits an orientation-based kinematic constraint in joints with two degrees of freedom (DoF), such as cardan joints, saddle joints, the human wrists, elbow or ankles. The method determines the relative heading of the joint segments in real time by minimization of a nonlinear cost function. A filter for singularity treatment ensures accurate tracking during motion phases for which the cost function minimum is ambiguous. We experimentally validate the method in metacarpophalangeal (MCP) joints between the palm and the fingers. Accurate relative orientation tracking is achieved continuously despite several singular motion phases and even though the heading components of the 6D orientations drift by more than 360 degrees within ten minutes. The proposed method overcomes a major limitation of inertial motion tracking and thereby facilitates the use of this technology in robotic and biomechanical applications.

We consider communication in systems where (1) it is desirable to reduce communication load and (2) the signals to be transmitted contain approximately periodic patterns. We propose methods that extract the periodic pattern from the signal in real time and reduce communication between sender and receiver to one sample per cycle period. The proposed methods allow for variable cycle duration and immediately return to full communication if the signal no longer contains the extracted pattern. During reduced communication the receiver provides an immediate real-time signal based on extrapolation and concatenation of the extracted pattern. At the end of each cycle, the receiver additionally reconstructs the signal segment of that cycle by means of time warping, which yields an output that is more accurate but is provided only cyclewise. As a specific example application we consider transmission of quaternion signals from wireless wearable inertial sensors attached to the lower body of a walking human. We quantify how repetitive these signals are and how much they change when the subject simulates pathological gait. We then determine the error between the true signal and the extrapolated and reconstructed signals. The proposed method is found to reduce communication load by up to 80% while remaining sensitive to deviations from the natural variability of human gait.

In inertial motion tracking of kinematic chains, inertial measurement units (IMUs) are attached to each segment in order to track their motion in three-dimensional space. Determining the relations between the functional axes of a joint and the local coordinate system of the attached sensor is a crucial requirement. For the case of hinge joints, methods have been proposed that exploit kinematic constraints to automatically identify the local hinge joint axis coordinates from the raw data of almost arbitrary motions. However, to current date, it remains unclear which joint motions are sufficiently rich for the joint axis to become identifiable. We consider a commonly used gyroscope-based kinematic constraint and present a novel accelerometer-based kinematic constraint. We study conditions of identifiability by analyzing the nonlinear constraint equations and present practical conditions for the minimum excitation that is required. Among other results, we prove that planar motions and subsequent motions of both ends of the joint are sufficient as long as the joint axis does not remain perfectly horizontal. Theoretical results are validated in experimental studies of a human upper limb wearing an exoskeleton. Despite the typical IMU-related measurement inaccuracies and although the human elbow joint is only an approximate hinge joint, the cost function defined by the kinematic constraints exhibits a distinct global minimum at the true joint axis coordinates if the motion fulfills the proposed requirements.

Many stroke patients suffer from the drop foot syndrome, which is characterized by a limited ability to lift (the lateral and/or medial edge of) the foot and leads to a pathological gait. In this contribution, we consider the treatment of this syndrome via functional electrical stimulation (FES) of the peroneal nerve during the swing phase of the paretic foot. A novel three-electrodes setup allows us to manipulate the recruitment of m. tibialis anterior and m. fibularis longus via two independent FES channels without violating the zero-net-current requirement of FES. We characterize the domain of admissible stimulation intensities that results from the nonlinearities in patients' stimulation intensity tolerance. To compensate most of the cross-couplings between the FES intensities and the foot motion, we apply a nonlinear controller output mapping. Gait phase transitions as well as foot pitch and roll angles are assessed in realtime by means of an Inertial Measurement Unit (IMU). A decentralized Iterative Learning Control (ILC) scheme is used to adjust the stimulation to the current needs of the individual patient. We evaluate the effectiveness of this approach in experimental trials with drop foot patients walking on a treadmill and on level ground. Starting from conventional stimulation parameters, the controller automatically determines individual stimulation parameters and thus achieves physiological foot pitch and roll angle trajectories within at most two strides.

We examine the usefulness of event-based sampling approaches for reducing communication in inertial-sensor-based analysis of human motion. To this end we consider realtime measurement of the knee joint angle during walking, employing a recently developed sensor fusion algorithm. We simulate the effects of different event-based sampling methods on a large set of experimental data with ground truth obtained from an external motion capture system. This results in a reduced wireless communication load at the cost of a slightly increased error in the calculated angles. The proposed methods are compared in terms of best balance of these two aspects. We show that the transmitted data can be reduced by 66% while maintaining the same level of accuracy.

Inertial sensor networks enable realtime gait analysis for a multitude of applications. The usability of inertial measurement units (IMUs), however, is limited by several restrictions, e.g. a fixed and known sensor placement. To enhance the usability of inertial sensor networks in every-day live, we propose a method that automatically determines which sensor is attached to which segment of the lower limbs. The presented method exhibits a low computational workload, and it uses only the raw IMU data of 3 s of walking. Analyzing data from over 500 trials with healthy subjects and Parkinson’s patients yields a correct-pairing success rate of 99.8% after 3 s and 100% after 5 s.

A growing number of researchers consider iterative learning control (ILC) a promising tool for numerous control problems in biomedical application systems. We will briefly discuss why classical ILC theory is technically too restrictive for some of these applications. Subsequently, we will extend the classical ILC design in the lifted systems framework to the class of repetitive trajectory tracking tasks with variable pass length. We will analyse the closed-loop dynamics for two standard learning laws, and we will discuss in which sense the tracking error can be reduced by which controller design strategies. Necessary and sufficient conditions for monotonic convergence will be derived. We then summarise all results in a set of practical controller design guidelines. Finally, a simulation study is presented, which demonstrates the usefulness of these guidelines and illustrates the special dynamics that occur in variable pass length learning.

Many stroke patients suffer from the drop foot syndrome, which is characterized by a limited ability to lift the foot and leads to a pathological gait. We consider treatment of this syndrome via Functional Electrical Stimulation (FES) of the peroneal nerve during the swing phase of the paretic foot. We highlight the role of feedback control for addressing the challenges that result from the large individuality and time-variance of muscle response dynamics. Unlike many previous approaches, we do not reduce the control problem to the scalar case. Instead, the entire pitch angle trajectory of the paretic foot is measured by means of a 6D Inertial Measurement Unit (IMU) and controlled by an Iterative Learning Control (ILC) scheme for variable-pass-length systems. While previously suggested controllers were often validated for the strongly simplified case of sitting or lying subjects, we demonstrate the effectiveness of the proposed approach in experimental trials with walking drop foot patients. Our results reveal that conventional trapezoidal stimulation intensity profiles may produce a safe foot lift, but often at the cost of too high intensities and an unphysiological foot pitch motion. Starting from such conservative intensity profiles, the proposed learning controller automatically achieves a desired foot motion within one or two strides and keeps adjusting the stimulation to compensate time-variant muscle dynamics and disturbances.

This editorial provides a concise introduction to the methods and applications of inertial sensors. We briefly describe the main characteristics of inertial sensors and highlight the broad range of applications as well as the methodological challenges. Finally, for the reader’s guidance, we give a succinct overview of the papers included in this special issue.

Communication load is a limiting factor in many real-time systems. Event-triggered state estimation and event-triggered learning methods reduce network communication by sending information only when it cannot be adequately predicted based on previously transmitted data. This paper proposes an event-triggered learning approach for nonlinear discrete-time systems with cyclic excitation. The method automatically recognizes cyclic patterns in data - even when they change repeatedly - and reduces communication load whenever the current data can be accurately predicted from previous cycles. Nonetheless, a bounded error between original and received signal is guaranteed. The cyclic excitation model, which is used for predictions, is updated hierarchically, i.e., a full model update is only performed if updating a small number of model parameters is not sufficient. A nonparametric statistical test enforces that model updates happen only if the cyclic excitation changed with high probability. The effectiveness of the proposed methods is demonstrated using the application example of wireless real-time pitch angle measurements of a human foot in a feedback-controlled neuroprosthesis. The experimental results show that communication load can be reduced by 70 % while the root-mean-square error between measured and received angle is less than 1{\deg}., Comment: 6 pages and 6 figures; to appear in IEEE Control Systems Letters

Robotics and Functional Electrical Stimulation (FES) are well-established technologies for the rehabilitation of stroke and spinal cord injured (SCI) patients. We propose a hybrid solution that combines feedback-controlled FES of biceps and triceps as well as posterior and anterior deltoid with a cable-driven robotic system to support repetitive arm movements, like “breaststroke swimming” exercises. The robotic system partially compensates the arm weight by controlling the cable tension forces, and the FES promotes motion in the transversal plane. To adjust the FES support to the needs of the individual patients we use an iterative learning vector field (ILVF) which encodes the stimulation intensities that are applied to guide the patient along a pre-specified reference trajectory in the joint angle space. In contrast to previous iterative learning control approaches, the ILVF allows the patient to perform the motion at self-selected cadence. The proposed learning algorithm explicitly takes the dynamics of the artificially activated muscles into account and assures smooth stimulation intensity profiles. The control algorithm is tested in simulations using a complex neuro-musculoskeletal model. For “breaststroke” motions, the initial RMS error of purely volitional movements is reduced from 38° to 10° within 21 cycles by the adaptive FES support. After 50 iterations of the ILVF, the algorithm converges to a steady state RMS error of 4°. Changes in the patient's muscle activity and cadence were well tolerated by the control system and did not cause a noticable increase in the steady state RMS error.

A pressure servo controller for pneumatic muscle actuators (PMAs) is presented and tested in three test scenarios. Unlike most previous non-linear control approaches, the current contribution demonstrated that a linear controller design becomes feasible after a slight reformulation of the non-linear plant dynamics. The controller is designed by pole placement in the discrete-time domain. It is demonstrated experimentally that the controller solves the control task and compensates pneumatic cross-coupling effects between different PMAs connected to a shared kinematic structure with an additional disturbance cancellation. When the initial PMA volume is kept constant, a closed-loop cutoff frequency of about 6 Hz is achieved. Beyond that, a sine pressure trajectory with 1 Hz is tracked successfully at time-varying volume, and the pressure changes triggered by volume changes are significantly reduced. The controller is easy to implement and therefore suitable to run on low-cost embedded hardware.

Rehabilitation robotics and neuromuscular stimulation have become widespread technologies for rehabilitation training of stroke and spinal cord injured patients. In this context, real-time tracking of the performed motion facilitates real-time control of the motion support and biofeedback about undesired compensatory motions. We consider a cable-driven robotic system for upper limb rehabilitation and extend it by two wearable inertial sensors. By sensor fusion of the robotic and inertial measurements, we obtain accurate estimates of the forearm and upper arm orientation and position, which cannot be obtained by either of both measurement systems alone. A real-time biofeedback is introduced to prevent undesired compensatory motions of the trunk and shoulder. The proposed methods are evaluated with respect to an optical reference system in a series of experimental trials with and without compensatory motions. Using only the robotic sensors yields average measurement errors of up to 24 cm for the shoulder position and 19° for the elbow angle. In contrast, the proposed hybrid sensor fusion achieves accuracies better than 6 cm and 4°, respectively.

Swinging up a pendulum on a cart is a well-known demonstration example for trajectory tracking in a nonlinear system. The standard realtime feedback control approach fails if the plant output is not available in real time, e.g. due to large or variable measurement delays. However, the task can be solved in multiple trials by applying feedforward inputs that are improved from trial to trial by Iterative Learning Control (ILC). Our examination demonstrates that an ILC can be used for trajectory tracking close to the singularities and the unstable equilibrium of a non-linear system. Specifically, we present an ILC algorithm for pendulum swing-up by angle trajectory tracking. The controller design is based on a modified plant inversion approach that restricts the learning process to trajectory segments with small tracking errors and sufficient input sensitivity. We show that these restrictions lead to improved learning progress in contrast to conventional learning from the complete trajectory. Controller performance is evaluated in an experimental testbed. The ILC starts from a zero-input trajectory and learns to swing up the pendulum within six iterations. Robustness is analyzed experimentally, and the performance is compared to literature results. The convergence is at least two orders of magnitude faster than the one achieved by other methods that avoid feedback and do not rely on a suitable initial input trajectory.