Background: Foot drop is characterised by impaired active ankle dorsiflexion, affecting gait quality through foot slap during loading response and reduced toe clearance during swing phase. While ankle-foot orthoses (AFOs) are commonly prescribed, traditional designs often limit natural ankle motion during push-off and cause discomfort. There is a need for AFO designs that provide targeted dorsiflexion assistance while maintaining full plantarflexion mobility. The Rise AFO represents a novel approach, featuring complete ankle mobility and personalised dorsiflexion assistance determined by device stiffness.
Objective: To evaluate the effect of Rise AFO stiffness in reducing tibialis anterior activation and increasing ankle dorsiflexion during loading response and swing phase in healthy participants.
Methods: Ten healthy participants completed a randomised crossover trial comparing seven walking conditions on a treadmill at 4.4 km/h: baseline (no AFO), five Rise AFO stiffness configurations (0.05-0.39 Nm/deg), and a sham device with no stiffness. Primary outcomes included tibialis anterior EMG activity and ankle dorsiflexion. Secondary measures were plantarflexor activity, and sagittal plane knee and hip kinematics. Statistical parametric mapping analysed differences across the gait cycle.
Results: Compared to baseline, all Rise AFO configurations significantly reduced tibialis anterior activity during swing phase. The four stiffest configurations significantly increased swing phase ankle dorsiflexion, demonstrating a linear relationship between stiffness and mid-swing dorsiflexion (R² = 0.87). Loading response kinematics and muscle activity remained unchanged. Plantarflexor activity and proximal joint kinematics were not adversely affected. The sham device had no effect.
Conclusion: The Rise AFO effectively assisted dorsiflexion during swing phase while preserving natural plantarflexion, demonstrating potential as a personalised treatment for foot drop.

Abstract: Gait event detection (GED) plays an important role in clinical gait analysis and rehabilitation. Real-time detection of temporal gait features within the gait cycle is essential for closed-loop control of wearable assistive devices and neuroprostheses. Machine-learning algorithms used in these systems require training datasets of real-world walking, which are currently collected using insole footswitches. However, insole footswitches suffer from performance and reliability issues. This study evaluates the use of inertial measurement units (IMUs) as an alternative for detecting foot-ground contact events during gait. The detection performance of five IMU placement locations and four rule-based detection algorithms was assessed in laboratory and real-world settings, using force plates and footswitches as ground-truth reference. In the laboratory, the heel-mounted IMU combined with a vertical jerk–based algorithm achieved an F1-score of 97.6% and a mean timing error of –1.5 ± 1.5 ms relative to force plates. Although insole footswitches achieved a comparable F1-score (97.2%), they showed significantly larger timing errors (17.7 ± 11.7 ms, p <
0.001). In the real-world environments, the heel-mounted IMU combined with a zero-velocity update–based detection algorithm achieved a mean timing error of –5.0 ± 23.4 ms relative to the footswitches. These findings show that a single heel-mounted IMU combined with a rule-based detection algorithm offers a practical and accurate alternative to insole footswitches for collecting gait datasets in real-world environments.

Muscle-tendon paths vary between individuals, and musculoskeletal models show sensitivity to these variations when estimating muscle and joint forces. Accurate estimations of these internal forces allow for a more comprehensive approach to researching debilitating musculoskeletal pathologies such as osteoarthritis. However, defining subject-specific muscle-tendon paths is labour-intensive and requires expert knowledge, which is not as repeatable. Therefore, in this study, the accuracy of determining subject-specific muscle points and volumes based on lower limb Magnetic Resonance (MR) scans with the nnU-net is evaluated. Two models are trained using the open-source pipeline, referred to as the point and volume model. The volume model aims to segment muscle-tendon volumes and is trained on the open-source augmented dataset of Henson et al. (2023). The point model localises attachment and via points describing muscle action lines for a subset of relevant muscles of the volume model. Since U-net is not designed for predicting points, a workaround is introduced by creating cubes around the points as labels. The 3D volume model scored a median Dice Similarity Coecient of 92.7 % and shows some generalisation capability. The 3D point model scored a median Euclidean error of 5.1 mm. Compared to intra-operator variability for attachment points, this approach yields lower and more repeatable accuracy without required manual intervention. The nnU-net pipeline is capable of producing accurate models that can define subject-specific muscle-tendon paths based on MR scans.

3D human motion capture can provide unique information on joint angles and position, and body segment lengths. It has many potential medical applications, including diagnostics, disease monitoring, and clinical decision making. Markerless motion capture makes motion capture accessible and has the potential to objectify motion analysis, and possibly reduce bias. However, neural networks for markerless motion capture can become biased by using unrepresentative training data. Mainstream datasets used for training do not provide detailed information about their subjects characteristics, like skin tone and clothing types per video, making it difficult to study their diversity. This study uses the OpenSim Driven Animated Human (ODAH) pipeline to create three datasets with increasingly diverse subjects and environments and assess the bias and generalization of a biomechanics-aware neural network. The Mean Per Joint Absolute Error (MPJAE), Procrustes Aligned Mean Per Joint Position Error (PA-MPJPE), and absolute relative scale error are determined per video and statistically analyzed together with the video's variables. Significant differences were found in the MPJAE and PA-MPJPE for videos with different motion types, subjects, skin tones, and blinds colors, and significant correlations were found with weight and BMI. The scale error differed significantly based on the subject, skin tone, environmental colors, weight, and height. The model performed significantly better on seen skin tones and environments than unseen skin tones and variables. This study has shown that it cannot be assumed that a neural network’s performance will generalize to different skin tones and different environmental colors, because this will result in greater errors. In future research, the created test data should be expanded to retrain the network and determine the effect of increasing training data diversity.

Lower-limb exoskeletons often use trajectory-tracking control to define the device's motion and assistance level. One challenge lies in ensuring a smooth and comfortable interaction between the user and the robotic device by defining a reference trajectory. While recent research has focused on generating individualized gait patterns based on user-specific body characteristics and walking speed, limited research has explored the subjective perception of these patterns and their impact on user experience and rehabilitation outcomes.

This study investigates user perceptions of individualized versus standard and random gait patterns, focusing on enjoyment, comfort, and naturalness. A predictive gait pattern model, incorporating individual data and walking speed, was developed and tested with human participants using a grounded robotic lower limb device. Participants compared the three gait pattern types and provided subjective feedback through a questionnaire.

Findings indicate no significant preference for any gait pattern in terms of enjoyment, comfort, and naturalness, except for physical strain where the predicted pattern caused significantly more strain than the standard. The analysis also revealed that longer engagement with the device led to increased comfort and naturalness, suggesting an adaptation effect. A general tendency towards preferring the standard pattern was noted, though further research is necessary to determine whether a larger sample size reveals significant differences. Additionally, the perception of different gait patterns and their effect on the rehabilitation outcome should be explored with stroke patients.

Biomechanics studies the underlying mechanisms between body movements and forces. Accurate motion data is crucial for the biomechanics. Currently, marker-based motion capture systems are often used by researchers to record motion data. Marker-based motion capture systems are not widely adopted due to its drawbacks in terms of financial and time costs, portability, etc. Video-based motion capture systems can record motions using videos collected by webcams, cameras, and smartphones as the input and then estimate human motions from those videos. A simpler setup makes video-based motion capture technology more accessible for widespread use. However, existing motion capture datasets commonly lack of biomechanically accurate annotation, resulting in a deficiency in the biomechanical accuracy of exsisting video-based motion capture methods. In the biomechanics community, there are a lot of validated and biomechanically accurate models and motion data; however, corresponding video data is lacking. We can construct human-like appearance based on these data and generate a synthetic human motion video dataset using 3D graphic software. In this thesis, we purposed a pipeline that can generate synthetic human motion videos. The pipeline takes subject-specific OpenSim model and motion as input and uses SMPL-X model to generate human-like appearance. We validated the synthetic data generated by our pipeline and demonstrated the biomechanical reliability of the pipeline. Using this pipeline, we created synthetic dataset ODAH with biomechanically accurate annotations for neural network training.

Trunk motor control is essential for the proper functioning of the upper extremities and is an important predictor of gait capacity in children with delayed development. Early diagnosis and intervention can potentially increase the trunk motor capabilities in later life. However, current tools used to assess the level of trunk motor control are largely observation-based and lack the sensitivity to change required to accurately monitor progress and effects of therapy in children below the age of 4. To the best of our knowledge, this is the first attempt to use trunk-attached inertial measurement units (IMUs) to differentiate different levels of trunk motor control in this population. We performed experiments with seven children to examine the applicability of the RMS of jerk as an outcome metric for the level of trunk motor control. This study showed that the root mean square (RMS) of jerk decreases for ages up to 24 months, is relatively independent of data segment and length, and shows results similar to a more established method: the centre of pressure (COP) velocity. These findings suggest that the RMS of jerk shows potential as a metric for the differentiation of different trunk motor control levels. However, due to the small sample size, a follow-up study is necessary to verify and validate these results.

Skateboarding involves a human controlling a four wheeled vehicle that is steered by tilting the standing surface. The riding mechanics of skateboarding have been well reported [2, 3]. The sport also includes aerial maneuvers such as jumping of stairs, flying off ramps and flipping and rotating the skateboard. The most basic aerial trick is called the ollie. The athlete jumps up while pushing down on the back end of the skateboard’s tail, causing a rotation about the back axle. The upward acceleration due to the rotation together with the tail-ground impact cause the skateboard to go airborne. Midair the athlete drags the skateboard up through frictional contact and levels it out to land the trick. The most concrete performance measure of the ollie is height according to the Olympic judging criteria [4]. To reach maximum height the dynamics such as impact, dynamic response, and torque production are dependent on shape, inertia and mass, which gives reason to assume an optimal shape exists. This leads to the research question: What are the optimal geometric and inertial parameters of a skateboard for an Olympic athlete to reach maximal ollie height. The skateboard geometry is optimized through multiphase direct collocation with the objective of maximal ollie height. A parameterized model is created with scaling mass and inertia properties such that the geometry of the skateboard. Modelling the dynamics of the ollie including impact and friction are done with a point mass human controller that is kineticly and kinematicly mapped to a counter movement jump. A simplistic contact implicit impact scheme is made for a higher order optimization. The ollie height is improved by changing the mass and inertia properties of the skateboard. Multiple optimal board shapes are generated for example a skateboard with a smaller wheelbase can reach higher ollie height compared to an industry standard skateboard.

Manipulating soft and fragile objects is a challenging task in robotic grasping. The key challenge for robotic grasping is to exert enough grip force to prevent slipping while being gentle enough to prevent damage to an object. Existing grippers used for processes like automatic harvesting of fruits, either apply excessive grip force leading to object damage or react to slip resulting in object release from the gripper. The aim of this study is to develop a grip force controller that uses tactile feedback to maintain a constant frictional safety margin over the minimum required grip force, called Safety Margin Control. Tactile sensors can provide information on friction, which is used to predict slip. An optical tactile sensor is modeled and used in simulations where Safety Margin Control regulates the grip force during interaction with various virtual objects. The deformation of the sensor’s soft viscoelastic membrane is described by local frictional behavior and used to estimate the safety margin. The desired safety margin is set to 30%, based on comparison to the way humans control grip force in their fingertips. The desired value can be tuned to favor release over damage and vice versa. Safety Margin Control is compared to two baseline controllers: React To Slip and Conservative Control. The performance is evaluated based on maximum pressure and total lateral displacement of the object relative to the sensor. Safety Margin Control results in a pressure decrease of 44% on average compared to Conservative Control, and no significant pressure change was observed compared to React To Slip. The total lateral displacement for Safety Margin Control is 0 mm, as opposed to 1.3 mm for React To Slip. Safety Margin Control provides a way forward for automated harvesting as the pressure exerted on an object can be reduced while no slip occurs.

Classification of pitch types outside the laboratory or game environment provide benefits in designing and outlining training routines to reduce injury and improve performance. Current classification approaches are based on vision-based optical data, however these optical data is often not available in training sessions. The aim of the current study is to use machine learning algorithms to classify pitch types, i.e., fastball, change-up and curveball, based on pelvis and trunk IMU data in training sessions. A total of 406 successful pitches thrown by 19 pitchers were used to classify pitch types. Three conditions were tested and evaluated in a binary (fastball versus others) and multi-class (fastball versus change-up versus curveball) classification approach. Condition A included the direct output of the PitchPerfect software, whereas condition B included features normalized by fastball characteristics. Condition C is a combination of both condition A and B. The random forest algorithm demonstrated the best predictions in both the binary and multi-class classification approach based on the highest accuracy and F1 scores, i.e., the harmonic mean between sensitivity and precision. Therefore, the random forest algorithm is the best fit for classifying pitch types based on pelvis and trunk IMU data in training sessions. In addition, the performance of the classification algorithm improved when using a binary classification approach. There were no relevant improvements when using additional features. The random forest algorithm can directly be implemented in the PitchPerfect application. Pitchers can use the pitch type data to track and tune their performances, whereas coaches can use the data to design match and training routines. Future research should focus on larger datasets, i.e., more pitchers and pitches, to pre-classify pitchers with similar pitching characteristics to improve the classification algorithms.

Robots can be powerful tools in post-stroke gait rehabilitation. However, state-of-the-art robots are often expensive machines containing rigid links with high inertia. Their expensiveness could limit their availability, and their high inertia reduces transparency, which could hinder rehabilitation progress. These factors raise the need for a minimalistic transparent robot that can effectively fill this gap. This research aims to design and validate such a device, a minimally actuated tendon-based exercise environment. The device is synthesized using an optimization algorithm that considers possible system configurations and optimizes both these configurations and their respective design parameters. Validation is done based on a reconstructed simulation of gait using motion capture on the optimal design to check whether it could be used for real-life rehabilitation. It was found that the most simplistic solution is not yet adequate for rehabilitation; thus, a slightly more complex design is required. While not providing the final solution, this research provides an important stepping stone towards designing a minimally actuated, simplistic, and transparent rehabilitation device.

Short-track speed skates are prepared with rocker to improve cornering behavior. However, rocker also has a negative impact on ice friction. As there is currently no scientific theory for selecting optimal rocker profiles, skaters have to rely on personal experience. There is a lack of experimental data available in literature to validate current hypotheses for ice friction. A setup was designed to measure ice friction of an upright skate with varying rocker profiles and normal loads. The experiment was conducted on the artificial ice rink at Thialf, Heerenveen. A sled fitted with two parallel blades was towed at a constant speed of approximately 1.5 ms-1. A force transducer was used to measure friction forces on the sled. Short-track and long-track blades with representative rocker radii were tested. A strong correlation was found between rocker radius and ice friction. Friction forces were found to be on average greater by a factor of 1.868(±0.050) for blades with rocker radius of 10 m compared to 21 m. The results further show high repeatability between tests. This study demonstrates the impact of rocker on ice friction in speed skating. Further research is needed to establish a valid theory for optimal rocker selection. This test method can be used in the future to include different variables, such as ice temperature, sliding speed or blade inclination angle. The method is not limited to short-track speed skating, but can be applied to other disciplines.

Active hand prostheses aid people, who suffer from an upper limb deficiency, in performing activities of daily living. The two types of active hand prostheses available are the myo-electric prosthesis and body-powered prosthesis. Even though both allow a user to grasp objects, the downsides are major. The high actuation force required for the operation of body-powered prostheses and the high costs and low feedback of the myo-electric prostheses lead to high abandon rates. A hybrid solution could combine the best of both worlds: high output force and transparent feedback. In this study we introduce a body-powered prosthetic hand with force assist, that allows for user operation without muscle fatigue, while still providing the user with transparent feedback.   Design requirements are set up following parameters from literature. For this study the outer shell and closing mechanism of the 100-Dollar Hand have been used and adapted to purpose. An extensive mechanical analyses, a new conceptual design and the addition of a micro controller led to the new closing mechanism of the Hybrid Hand. The proposed prototype has been tested on a test bench and results have been compared to the conventional body-powered 100-Dollar Hand. The Hybrid Hand decreases the actuation forces needed for a pinch force of 15 N with 33% in comparison to the 100-Dollar Hand. Moreover, the system increases the output pinch force at 80 N actuation force with almost 80%. With a weight of 320 gram and costs of under 200 dollar, the hand can amplify the pinch force with a maximum of 29 N. However, due to the low efficiency of the worm gear, the opening time of the hand (3.5 s) has to be improved. This paper presents the first 3D printed hand prosthesis with force assist that allows for user operation without muscle fatigue. With a low weight, low cost, high pinch force the prosthesis is accessible to and could aid people with an upper limb deficiency all over the world. Future research should further investigate the durability of the system and should introduce ways to improve and optimize the efficiency of the transmission of the system.