Exoskeleton devices impose kinematic constraints on a user's motion and affect their stability due to added mass but also due to the simplified mechanical design. This paper investigates how these constraints resulting from simplified mechanical designs impact the gait kinematics and stability of users by wearing an ankle exoskeleton with changeable degree of freedom (DoF). The exoskeleton used in this paper allows one, two, or three DoF at the ankle, simulating different levels of mechanical complexity. This effect was evaluated in a pilot study consisting of six participants walking on a straight path. The results show that increasing the exoskeleton DoF results in an improvement of several metrics, including kinematics and gait parameters. The transition from 1 DoF to 2 DoF is shown to have a larger effect than the transition from 2 DoF to 3 DoF for an ankle exoskeleton. However, an exoskeleton with 3 DoF at the ankle featured the best results. Increasing the number of DoF resulted in stability values closer the values when walking without the exoskeleton, despite the added weight of the exoskeleton.

Gait benchmark empowers uncounted encouraging research fields such as gait recognition, humanoid locomotion, etc. Despite the growing focus on gait analysis, the research community is hindered by the limitations of the currently available databases, which mostly consist of videos or images with limited labeling. In this paper, we introduce GaitMotion, a multitask dataset leveraging wearable sensors to capture the patients' real-time movement with pathological gait. This dataset offers extensive ground-truth labeling for multiple tasks, including step/stride segmentation and step/stride length prediction, empowers researchers with a more holistic understanding of gait disturbances linked to neurological impairments. The wearable gait analysis suit captures the gait cycle, pattern, and parameters for both normal and pathological subjects. This data may prove beneficial for healthcare products focused on patient progress monitoring and post-disease recovery, as well as for forensics technologies aimed at person reidentification, and biomechanics research to aid in the development of humanoid robotics. Moreover, the analysis has considered the drift in data distribution across individual subjects. This drift can be attributed to each participant's unique behavioral habits or potential displacement of the sensor. Stride length variance for normal, Parkinson's, and stroke patients are compared to recognize the pathological walking pattern. As the baseline and benchmark, we provide an error of 14.1, 13.3, and 12.2 centimeters of stride length prediction for normal, Parkinson's, and Stroke gaits separately. We also analyzed the gait characteristics for normal and pathological gaits in terms of the gait cycle and gait parameters.

Model-based approaches for planning and control for bipedal locomotion have a long history of success. It can provide stability and safety guarantees while being effective in accomplishing many locomotion tasks. Model-free reinforcement learning, on the other hand, has gained much popularity in recent years due to computational advancements. It can achieve high performance in specific tasks, but it lacks physical interpretability and flexibility in re-purposing the policy for a different set of tasks. For instance, we can initially train a neural network (NN) policy using velocity commands as inputs. However, to handle new task commands like desired hand or footstep locations at a desired walking velocity, we must retrain a new NN policy. In this work, we attempt to bridge the gap between these two bodies of work on a bipedal platform. We formulate a model-based reinforcement learning problem to learn a reduced-order model (ROM) within a model predictive control (MPC). Results show a 49% improvement in viable task region size and a 21% reduction in motor torque cost. All videos and code are available at https://sites.google.com/view/ymchen/research/rl-for-roms.

We describe a new class of subsampling techniques for CNNs, termed multisampling, that significantly increases the amount of information kept by feature maps through subsampling layers. One version of our method, which we call checkered subsampling, significantly improves the accuracy of state-of-the-art architectures such as DenseNet and ResNet without any additional parameters and, remarkably, improves the accuracy of certain pretrained ImageNet models without any training or fine-tuning. We glean possible insight into the nature of data augmentations and demonstrate experimentally that coarse feature maps are bottlenecking the performance of neural networks in image classification.

People with Parkinson's Disease experience gait impairments that significantly impact their quality of life. Visual, auditory, and tactile cues can alleviate gait impairments, but they can become less effective due to the progressive nature of the disease and changes in people's motor capability. In this study, we develop a human-in-the-loop (HIL) framework that monitors two key gait parameters, stride length and cadence, and continuously learns a person-specific model of how the parameters change in response to the feedback. The model is then used in an optimization algorithm to improve the gait parameters. This feasibility study examines whether auditory cues can be used to influence stride length in people without gait impairments. The results demonstrate the benefits of the HIL framework in maintaining people's stride length in the presence of a secondary task.

This paper introduces a new approach to studying bipedal locomotion. The approach is based on magnetically actuated miniature robots. Building prototypes of bipedal locomotion machines has been very costly and overly complicated. We demonstrate that a magnetically actuated 0.3~gm robot, we call Big Foot, can be used to test fundamental ideas without necessitating very complex and expensive bipedal machines. We explore analytically and experimentally two age old questions in bipedal locomotion: 1. Can such robots be driven with pure hip actuation. 2. Is it better to use continuous or impulsive actuation schemes. First, a numerical model has been developed in order to study the dynamics and stability of a magnetically actuated miniature robot. We particularly focus on stability and performance metrics. Then, these results are tested using Big Foot. Pure hip actuation has been successful in generating gait on uphill surfaces. In addition, complex tasks such as following prescribed gait trajectories and navigating through a maze has been successfully performed by the experimental prototype. The nature and timing of hip torques are also studied. Two actuation schemes are used: Heel Strike Actuation and Constant Pulse Wave Actuation. With each scheme, we also vary the time duration of the applied magnetic field. Heel Strike actuation is found to have superior stability, more uniform gait generation, and faster locomotion than the Constant Pulse Wave option. But, Constant Pulse Wave achieves locomotion on steeper slopes.

Positive biomechanical outcomes have been reported with lower-limb exoskeletons in laboratory settings, but these devices have difficulty delivering appropriate assistance in synchrony with human gait as the task or rate of phase progression change in real-world environments. This paper presents a controller for an ankle exoskeleton that uses a data-driven kinematic model to continuously estimate the phase, phase rate, stride length, and ground incline states during locomotion, which enables the real-time adaptation of torque assistance to match human torques observed in a multi-activity database of 10 able-bodied subjects. We demonstrate in live experiments with a new cohort of 10 able-bodied participants that the controller yields phase estimates comparable to the state of the art, while also estimating task variables with similar accuracy to recent machine learning approaches. The implemented controller successfully adapts its assistance in response to changing phase and task variables, both during controlled treadmill trials (N=10, phase RMSE: 4.8 +- 2.4\%) and a real-world stress test with extremely uneven terrain (N=1, phase RMSE: 4.8 +- 2.7\%).

A convolution layer provides a method of producing a feature map from a two-dimensional input. This is accomplished by running a filter over the input data. The filter is just a set of weights that must be trained to identify a feature in regions of the input data. These features can be things like edges, points, or more complex information. The filter will have dimensional constraints that indicate width and height, and it will scan over the input data.

A convolution layer provides a method of producing a feature map from a two-dimensional input. This is accomplished by running a filter over the input data. The filter is just a set of weights that must be trained to identify a feature in regions of the input data. These features can be things like edges, points, or more complex information. The filter will have dimensional constraints that indicate width and height, and it will scan over the input data.