Modeling individual-specific gait dynamics based on kinematic data could aid the development of gait rehabilitation robotics by enabling robots to predict the user’s gait kinematics with and without external inputs, such as mechanical or electrical perturbations. Here we address a current limitation of data-driven gait models, which do not yet predict human gait dynamics nor responses to perturbations. We used Switched Linear Dynamical Systems (SLDS) to model joint angle kinematic data from healthy individuals walking on a treadmill during normal gait and during gait perturbed by functional electrical stimulation (FES) to the ankle muscles. Our SLDS models were able to generate joint angle trajectories in each of four gait phases, as well as across an entire gait cycle, given initial conditions and gait phase information. Because the SLDS dynamics matrices encoded significant coupling across joints that differed across individuals, we compared the SLDS predictions to that of a kinematic model, where the joint angles were independent. Joint angle trajectories generated by SLDS and kinematic models were similar over time horizons of a few milliseconds, but SLDS models provided better predictions of gait kinematics over time horizons of up to a second. We also demonstrated that SLDS models can infer and predict individual-specific responses to FES during swing phase. As such, SLDS models may be a promising approach for online estimation and control of and human gait dynamics, allowing robotic control strategies to be tailored to an individual’s specific gait coordination patterns.
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Data-Driven Gait Segmentation for Walking Assistance in a Lower-Limb Assistive Device
Hybrid systems, such as bipedal walkers, are challenging to control because of discontinuities in their nonlinear dynamics. Little can be predicted about the systems’ evolution without modeling the guard conditions that govern transitions between hybrid modes, so even systems with reliable state sensing can be difficult to control. We propose an algorithm that allows for determining the hybrid mode of a system in real-time using data-driven analysis. The algorithm is used with data-driven dynamics identification to enable model predictive control based entirely on data. Two examples—a simulated hopper and experimental data from a bipedal walker—are used. In the context of the first example, we are able to closely approximate the dynamics of a hybrid SLIP model and then successfully use them for control in simulation. In the second example, we demonstrate gait partitioning of human walking data, accurately differentiating between stance and swing, as well as selected subphases of swing. We identify contact events, such as heel strike and toe-off, without a contact sensor using only kinematics data from the knee and hip joints, which could be particularly useful in providing online assistance during walking. Our algorithm does not assume a predefined gait structure or gait phase transitions, lending itself to segmentation of both healthy and pathological gaits. With this flexibility, impairment-specific rehabilitation strategies or assistance could be designed.
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- Award ID(s):
- 1637764
- NSF-PAR ID:
- 10109548
- Date Published:
- Journal Name:
- International Conference on Robotics and Automation
- Page Range / eLocation ID:
- 1390 to 1396
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Modeling individual-specific gait dynamics based on kinematic data could aid development of gait rehabilitation robotics by enabling robots to predict the user’s gait kinematics with and without external inputs, such as mechanical or electrical perturbations. Here we address a current limitation of data driven gait models, which do not yet predict human responses to perturbations. We used Switched Linear Dynamical Systems (SLDS) to model joint angle kinematic data from healthy individuals walking on a treadmill during normal gait and during gait perturbed by functional electrical stimulation (FES) to the ankle muscles. Our SLDS models were able to predict the time-evolution of joint kinematics in each of four gait phases, as well as across an entire gait cycle. Because the SLDS dynamics matrices encoded significant coupling across joints, we compared the SLDS predictions to that of a kinematic model, where the joint angles were independent. Gait kinematics predicted by SLDS and kinematic models were similar over time horizons of a few milliseconds, but SLDS models provided better predictions of gait kinematics over time horizons of up to a second. We also demonstrated that SLDS models can infer and predict individual-specific responses to FES during swing phase. As such, SLDS models may be a promising approach for online estimation and control of and human gait dynamics, allowing robotic control strategies to be tailored to an individual’s specific gait coordination patterns.more » « less
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Detection of the user’s walking is a critical part of exoskeleton technology for the full automation of smooth and seamless assistance during movement transitions. Researchers have taken several approaches in developing a walk detection system by using different kinds of sensors; however, only a few solutions currently exist which can detect these transitions using only the sensors embedded on a robotic hip exoskeleton (i.e., hip encoders and a trunk IMU), which is a critical consideration for implementing these systems in-the-loop of a hip exoskeleton controller. As a solution, we explored and developed two walk detection models that implemented a finite state machine as the models switched between walking and standing states using two transition conditions: stand-to-walk and walk-to-stand. One of our models dynamically detected the user’s gait cycle using two hip encoders and an IMU; the other model only used the two hip encoders. Our models were developed using a publicly available dataset and were validated online using a wearable sensor suite that contains sensors commonly embedded on robotic hip exoskeletons. The two models were then compared with a foot contact estimation method, which served as a baseline for evaluating our models. The results of our online experiments validated the performance of our models, resulting in 274 ms and 507 ms delay time when using the HIP+IMU and HIP ONLY model, respectively. Therefore, the walk detection models established in our study achieve reliable performance under multiple locomotive contexts without the need for manual tuning or sensors additional to those commonly implemented on robotic hip exoskeletons.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICRA.2019.8794416
