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Human kinetics, specifically joint moments and ground reaction forces (GRFs) can provide important clinical information and can be used to control assistive devices. Traditionally, collection of kinetics is mostly limited to the lab environment because it relies on data that measured from a motion capture system and floor-embedded force plates to calculate the dynamics via musculoskeletal models. This spatially limited method makes it extremely challenging to measure kinetics outside the laboratory in a variety of walking conditions due to the expensive device setup and large space required. Recently, employing machine learning with IMU sensors are suggested as an alternative method for biomechanical analyses. Although these methods enable estimating human kinetic data outside the laboratory by linking IMU sensor data with kinetics dataset, they were limited to show inaccurate kinetic estimates even in highly repeatable single walking conditions due to the employment of generic deep learning algorithms. Thus, this paper proposes a novel deep learning model, Kinetics-FM-DLR-Ensemble-Net to estimate the hip, knee, and ankle joint moments in the sagittal plane and 3 dimensional ground reaction forces (GRFs) using three IMU sensors on the thigh, shank, and foot under several representative walking conditions in daily living, such as treadmill, level-ground, stair, and ramp with different walking speeds. This is the first study that implements both joint moments and GRFs in multiple walking conditions using IMU sensors via deep learning. Our deep learning model is versatile and accurate for identifying human kinetics across diverse subjects and walking conditions and our model outperforms state-of-the-art deep learning model for kinetics estimation by a large margin.

Powered ankle prostheses have been designed to reduce the energetic burden that individuals with transtibial amputation experience during ambulation. There is an open question regarding how much power the prosthesis should provide, and whether approximating biological ankle kinetics is optimal to reduce the metabolic cost of users. We tested 10 individuals with transtibial amputation walking on a treadmill wearing the BiOM powered ankle prosthesis programmed with 6 different power settings (0–100%), including a prosthetist-chosen setting, chosen to approximate biological ankle kinetics. We measured subjects’ metabolic cost of transport (COT) and the BiOM’s net ankle work during each condition. Across participants, power settings greater than 50% resulted in lower COT than 0% or 25%. The relationship between power setting, COT, and net ankle work varied considerably between subjects, possibly due to individual adaptation and exploitation of the BiOM’s reflexive controller. For all subjects, the best tested power setting was higher than the prosthetist-chosen setting, resulting in a statistically significant and meaningful difference in COT between the best tested and prosthetist-chosen power settings. The results of this study demonstrate that individuals with transtibial amputation may benefit from prescribed prosthetic ankle push-off work that exceeds biological norms.

Background This case study examines the influence of an Ankle Foot Orthosis Footwear Combination (AFO-FC) on musculotendon lengths and gait kinematics and kinetics after right thrombotic stroke resulting in left hemiplegia. Case Description and Methods Gait analysis was performed over three visits where the subject walked with an AFO-FC with two shank-to-vertical alignments, a posterior leaf spring AFO (PLS AFO), and shoes alone. Musculotendon lengths, kinematics, and kinetics were evaluated for each condition. Finding and Outcomes The AFO-FC improved walking speed and non-paretic kinematics compared to the PLS AFO and shoes alone. The operating length of the paretic gastrocnemius decreased with the AFO-FC improving knee kinematics in swing, but not stance. Conclusion Musculoskeletal modeling demonstrated that AFO-FCs altered gastrocnemius operating length during post-stroke hemiplegic gait. Using these tools to evaluate muscle operating lengths can provide insight into underlying mechanisms that may improve gait and guide future AFO-FC design. Clinical Relevance Modeling musculotendon operating length during movement has the potential to provide additional information about how AFO-FCs effect stiff muscles and improve mobility after stroke.