An official website of the United States government
Objective: Real-time measurement of biological joint moment could enhance clinical assessments and generalize exoskeleton control. Accessing joint moments outside clinical and laboratory settings requires harnessing non-invasive wearable sensor data for indirect estimation. Previous approaches have been primarily validated during cyclic tasks, such as walking, but these methods are likely limited when translating to non-cyclic tasks where the mapping from kinematics to moments is not unique. Methods: We trained deep learning models to estimate hip and knee joint moments from kinematic sensors, electromyography (EMG), and simulated pressure insoles from a dataset including 10 cyclic and 18 non-cyclic activities. We assessed estimation error on combinations of sensor modalities during both activity types. Results: Compared to the kinematics-only baseline, adding EMG reduced RMSE by 16.9% at the hip and 30.4% at the knee (p<0.05) and adding insoles reduced RMSE by 21.7% at the hip and 33.9% at the knee (p<0.05). Adding both modalities reduced RMSE by 32.5% at the hip and 41.2% at the knee (p<0.05) which was significantly higher than either modality individually (p<0.05). All sensor additions improved model performance on non-cyclic tasks more than cyclic tasks (p<0.05). Conclusion: These results demonstrate that adding kinetic sensor information through EMG or insoles improves joint moment estimation both individually and jointly. These additional modalities are most important during non-cyclic tasks, tasks that reflect the variable and sporadic nature of the real-world. Significance: Improved joint moment estimation and task generalization is pivotal to developing wearable robotic systems capable of enhancing mobility in everyday life.
more »
« less
Anticipation and Delayed Estimation of Sagittal Plane Human Hip Moments using Deep Learning and a Robotic Hip Exoskeleton
Estimating human joint moments using wearable sensors has utility for personalized health monitoring and generalized exoskeleton control. Data-driven models have potential to map wearable sensor data to human joint moments, even with a reduced sensor suite and without subject-specific calibration. In this study, we quantified the RMSE and R 2 of a temporal convolutional network (TCN), trained to estimate human hip moments in the sagittal plane using exoskeleton sensor data (i.e., a hip encoder and thigh- and pelvis-mounted inertial measurement units). We conducted three analyses in which we iteratively retrained the network while: 1) varying the input sequence length of the model, 2) incorporating noncausal data into the input sequence, thus delaying the network estimates, and 3) time shifting the labels to train the model to anticipate (i.e., predict) human hip moments. We found that 930 ms of causal input data maintained model performance while minimizing input sequence length (validation RMSE and R 2 of 0.141±0.014 Nm/kg and 0.883±0.025, respectively). Further, delaying the model estimate by up to 200 ms significantly improved model performance compared to the best causal estimators (p<0.05), improving estimator fidelity in use cases where delayed estimates are acceptable (e.g., in personalized health monitoring or diagnoses). Finally, we found that anticipating hip moments further in time linearly increased model RMSE and decreased R 2 (p<0.05); however, performance remained strong (R 2 >0.85) when predicting up to 200 ms ahead.
more »
« less
- PAR ID:
- 10470116
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-2365-8
- Page Range / eLocation ID:
- 12679 to 12685
- Format(s):
- Medium: X
- Location:
- London, United Kingdom
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Autonomous lower-limb exoskeletons must modulate assistance based on locomotion mode (e.g., ramp or stair ascent) to adapt to the corresponding changes in human biological joint dynamics. However, current mode classification strategies for exoskeletons often require user-specific tuning, have a slow update rate, and rely on additional sensors outside of the exoskeleton sensor suite. In this study, we introduce a deep convolutional neural network-based locomotion mode classifier for hip exoskeleton applications using an open-source gait biomechanics dataset with various wearable sensors. Our approach removed the limitations of previous systems as it is 1) subject-independent (i.e., no user-specific data), 2) capable of continuously classifying for smooth and seamless mode transitions, and 3) only utilizes minimal wearable sensors native to a conventional hip exoskeleton. We optimized our model, based on several important factors contributing to overall performance, such as transition label timing, model architecture, and sensor placement, which provides a holistic understanding of mode classifier design. Our optimized DL model showed a 3.13% classification error (steady-state: 0.80 ± 0.38% and transitional: 6.49 ± 1.42%), outperforming other machine learning-based benchmarks commonly practiced in the field (p<0.05). Furthermore, our multi-modal analysis indicated that our model can maintain high performance in different settings such as unseen slopes on stairs or ramps. Thus, our study presents a novel locomotion mode framework, capable of advancing robotic exoskeleton applications toward assisting community ambulation.more » « less
-
Robotic lower limb exoskeletons have been shown to successfully provide joint torques to assist human subjects during walking. Assisting the wearer during gait perturbations to prevent falls still poses a challenge due to specific requirements of the device, and complex bipedal dynamics of recovery. In this study, we present a hip exoskeleton device with pneumatically actuated abduction/adduction motion to provide hip torque for assisting with lateral balance. The device was designed to be wearable, allow integration with previously developed wearable gait perturbation detection system and knee exoskeleton, and produce fast actuation to provide assistive joint torque during gait perturbations. We present the results of the experimental benchtop tests of the device. The maximum torque output and rate of torque development were characterized using a load cell. The maximum angular displacement, with added weights to simulate the leg inertia, was recorded using an inertial measurement unit sensor. Lastly, a preliminary test on a human subject demonstrated that the device, when exerting instantaneous hip abduction torque during swing walking gait, can effectively modify foot placement in the lateral direction. This work contributes towards developing exoskeleton control strategies for assistance during gait perturbations to prevent falls.more » « less
-
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
-
Objective: Semi-active exoskeletons combining lightweight, low powered actuators and passive-elastic elements are a promising approach to portable robotic assistance during locomotion. Here, we introduce a novel semi-active hip exoskeleton concept and evaluate human walking performance across a range of parameters using a tethered robotic testbed. Methods : We emulated semi-active hip exoskeleton (exo) assistance by applying a virtual torsional spring with a fixed rotational stiffness and an equilibrium angle established in terminal swing phase (i.e., via pre-tension into stance). We performed a 2-D sweep of spring stiffness x equilibrium position parameters (30 combinations) across walking speed (1.0, 1.3, and 1.6 m/s) and measured metabolic rate to identify device parameters for optimal metabolic benefit. Results : At each speed, optimal exoskeleton spring settings provided a ∼10% metabolic benefit compared to zero-impedance (ZI). Higher walking speeds required higher exoskeleton stiffness and lower equilibrium angle for maximal metabolic benefit. Optimal parameters tuned to each individual (user-dependent) provided significantly larger metabolic benefit than the average-best settings (user-independent) at all speeds except the fastest (p = 0.021, p = 0.001, and p = 0.098 at 1.0, 1.3, and 1.6 m/s, respectively). We found significant correlation between changes in user's muscle activity and changes in metabolic rate due to exoskeleton assistance, especially for muscles crossing the hip joint. Conclusion : A semi-active hip exoskeleton with spring-parameters personalized to each user could provide metabolic benefit across functional walking speeds. Minimizing muscle activity local to the exoskeleton is a promising approach for tuning assistance on-line on a user-dependent basis.more » « less
