Objective: Powered assistive devices need improved control intuitiveness to enhance their clinical adoption. Therefore, the intent of individuals should be identified and the device movement should adhere to it. Skeletal muscles contract synergistically to produce defined lower-limb movements so unique contraction patterns in lower-extremity musculature may provide a means of device joint control. Ultrasound (US) imaging enables direct measurement of the local deformation of muscle segments. Hence, the objective of this study was to assess the feasibility of using US to estimate human lower-limb movements. Methods: A novel algorithm was developed to calculate US features of the rectus femoris muscle during a non-weight-bearing knee flexion/extension experiment by 9 able-bodied subjects. Five US features of the skeletal muscle tissue were studied, namely, thickness, angle between aponeuroses, pennation angle, fascicle length, and echogenicity. A multiscale ridge filter was utilized to extract the structures in the image and a random sample consensus (RANSAC) model was used to segment muscle aponeuroses and fascicles. A localization scheme further guided RANSAC to enable tracking in an US image sequence. Gaussian process regression (GPR) models were trained using segmented features to estimate both knee joint angle and angular velocity. Results: The proposed segmentation-estimation approach could estimate knee joint angle and angular velocity with an average root mean square error value of 7.45 degrees and 0.262 rad/s, respectively. The average processing rate was 3-6 frames per second that is promising towards real-time implementation. Conclusion: Experimental results demonstrate the feasibility of using US to estimate human lower-extremity motion. The ability of algorithm to work in real-time may enable the use of US as a neural interface for lower-limb applications. Significance: Intuitive intent recognition of human lower-extremity movements using wearable US imaging, may enable volitional assistive device control, and enhance locomotor outcomes for those with mobility impairments.

Background: Selection of prosthesis mechanical characteristics to restore function of persons with lower limb loss can be framed as an optimization problem to satisfy a given performance objective. However, the choice of a particular objective is critical, and considering only device and generalizable outcomes across users without accounting for inherent motor performance likely restricts a given patient from fully realizing the benefits of a prosthetic intervention. Objectives: This review presents methods for optimizing passive below-knee prosthesis designs to maximize rehabilitation outcomes and how considerations on patient motor performance may enhance these outcomes. Major Findings: Available literature supports that considering patient-specific variables pertaining to motor performance permits a multidimensional landscape relating device characteristics and user function, which may yield more accurate predictions of rehabilitation outcomes for individual patients. Moreover, the addition of targeted physical therapeutic interventions that encourage user self-organization may further improve these outcomes. We note the potential of existing paradigms to address these additional dimensions, and we encourage investigators to consider the many different performance objectives available for prosthesis optimization. Conclusions: By considering user motor performance in combination with prosthesis mechanical characteristics, a staged optimization approach can be formulated which acknowledges that device modifications may only improve outcomes to a certain extent and user self-organization is a critical component to complete rehabilitation. An iterative process that can be integrated within existing rehabilitative practices accounts for changes in patient status through combined targeted prosthetic solutions and physical therapeutic techniques, and embodies the concept of personalized intervention for patients with lower limb loss.

Advances in powered assistive device technology, including the ability to provide net mechanical power to multiple joints within a single device, have the potential to dramatically improve the mobility and restore independence to their users. However, these devices rely on the ability of their users to continuously control multiple powered lower-limb joints simultaneously. Success of such approaches rely on robust sensing of user intent and accurate mapping to device control parameters. Here, we compare two non-invasive sensing modalities: surface electromyography and sonomyography, (i.e., ultrasound imaging of skeletal muscle), as inputs to Gaussian process regression models trained to estimate hip, knee and ankle joint moments during varying forms of ambulation. Experiments were performed with ten non-disabled individuals instrumented with surface electromyography and sonomyography sensors while completing trials of level, incline (10°) and decline (10°) walking. Results suggest sonomyography of muscles on the anterior and posterior thigh can be used to estimate hip, knee and ankle joint moments more accurately than surface electromyography. Furthermore, these results can be achieved by training Gaussian process regression models in a task-independent manner; i.e., incorporating features of level and ramp walking within the same predictive framework. These findings support the integration of sonomyographic and electromyographic sensing within powered assistive devices to continuously control joint torque.

Clinical translation of “intelligent” lower-limb assistive technologies relies on robust control interfaces capable of accurately detecting user intent. To date, mechanical sensors and surface electromyography (EMG) have been the primary sensing modalities used to classify ambulation. Ultrasound (US) imaging can be used to detect user-intent by characterizing structural changes of muscle. Our study evaluates wearable US imaging as a new sensing modality for continuous classification of five discrete ambulation modes: level, incline, decline, stair ascent, and stair descent ambulation, and benchmarks performance relative to EMG sensing. Ten able-bodied subjects were equipped with a wearable US scanner and eight unilateral EMG sensors. Time-intensity features were recorded from US images of three thigh muscles. Features from sliding windows of EMG signals were analyzed in two configurations: one including 5 EMG sensors on muscles around the thigh, and another with 3 additional sensors placed on the shank. Linear discriminate analysis was implemented to continuously classify these phase-dependent features of each sensing modality as one of five ambulation modes. US-based sensing statistically improved mean classification accuracy to 99.8% (99.5-100% CI) compared to 8-EMG sensors (85.8%; 84.0-87.6% CI) and 5-EMG sensors (75.3%; 74.5-76.1% CI). Further, separability analyses show the importance of superficial and deep US information for stair classification relative to other modes. These results are the first to demonstrate the ability of US-based sensing to classify discrete ambulation modes, highlighting the potential for improved assistive device control using less widespread, less superficial and higher resolution sensing of skeletal muscle.

Lower-limb powered prostheses can provide users with volitional control of ambulation. To accomplish this goal, they require a sensing modality that reliably interprets user intention to move. Surface electromyography (EMG) has been previously proposed to measure muscle excitation and provide volitional control to upper- and lower-limb powered prosthesis users. Unfortunately, EMG suffers from a low signal to noise ratio and crosstalk between neighboring muscles, often limiting the performance of EMG-based controllers. Ultrasound has been shown to have better resolution and specificity than surface EMG. However, this technology has yet to be integrated into lower-limb prostheses. Here we show that A-mode ultrasound sensing can reliably predict the prosthesis walking kinematics of individuals with a transfemoral amputation. Ultrasound features from the residual limb of 9 transfemoral amputee subjects were recorded with A-mode ultrasound during walking with their passive prosthesis. The ultrasound features were mapped to joint kinematics through a regression neural network. Testing of the trained model against untrained kinematics from an altered walking speed show accurate predictions of knee position, knee velocity, ankle position, and ankle velocity, with a normalized RMSE of 9.0 ± 3.1%, 7.3 ± 1.6%, 8.3 ± 2.3%, and 10.0 ± 2.5% respectively. This ultrasound-based prediction suggests that A-mode ultrasound is a viable sensing technology for recognizing user intent. This study is the first necessary step towards implementation of volitional prosthesis controller based on A-mode ultrasound for individuals with transfemoral amputation.