Virtual reality-enhanced walking in people post-stroke: effect of optic flow speed and level of immersion on the gait biomechanics.

BACKGROUND: Optic flow-the apparent visual motion experienced while moving-is absent during treadmill walking. With virtual reality (VR), optic flow can be controlled to mediate alterations in human walking. The aim of this study was to investigate (1) the effects of fully immersive VR and optic flow speed manipulation on gait biomechanics, simulator sickness, and enjoyment in people post-stroke and healthy people, and (2) the effects of the level of immersion on optic flow speed and sense of presence. METHODS: Sixteen people post-stroke and 16 healthy controls performed two VR-enhanced treadmill walking sessions: the semi-immersive GRAIL session and fully immersive head-mounted display (HMD) session. Both consisted of five walking trials. After two habituation trials (without and with VR), participants walked three more trials under the following conditions: matched, slow, and fast optic flow. Primary outcome measures were spatiotemporal parameters and lower limb kinematics. Secondary outcomes (simulator sickness, enjoyment, and sense of presence) were assessed with the Simulator Sickness Questionnaire, Visual Analogue Scales, and Igroup Presence Questionnaire. RESULTS: When walking with the immersive HMD, the stroke group walked with a significantly slower cadence (-3.69strides/min, p = 0.006), longer stride time (+ 0.10 s, p = 0.017) and stance time for the unaffected leg (+ 1.47%, p = 0.001) and reduced swing time for the unaffected leg (- 1.47%, p = 0.001). Both groups responded to the optic flow speed manipulation such that people accelerated with a slow optic flow and decelerated with a fast optic flow. Compared to the semi-immersive GRAIL session, manipulating the optic flow speed with the fully immersive HMD had a greater effect on gait biomechanics whilst also eliciting a higher sense of presence. CONCLUSION: Adding fully immersive VR while walking on a self-paced treadmill led to a more cautious gait pattern in people post-stroke. However, walking with the HMD was well tolerated and enjoyable. People post-stroke altered their gait parameters when optic flow speed was manipulated and showed greater alterations with the fully-immersive HMD. Further work is needed to determine the most effective type of optic flow speed manipulation as well as which other principles need to be implemented to positively influence the gait pattern of people post-stroke. TRIAL REGISTRATION NUMBER: The study was pre-registered at ClinicalTrials.gov (NCT04521829).

On-Device Execution of Deep Learning Models on HoloLens2 for Real-Time Augmented Reality Medical Applications.

The integration of Deep Learning (DL) models with the HoloLens2 Augmented Reality (AR) headset has enormous potential for real-time AR medical applications. Currently, most applications execute the models on an external server that communicates with the headset via Wi-Fi. This client-server architecture introduces undesirable delays and lacks reliability for real-time applications. However, due to HoloLens2's limited computation capabilities, running the DL model directly on the device and achieving real-time performances is not trivial. Therefore, this study has two primary objectives: (i) to systematically evaluate two popular frameworks to execute DL models on HoloLens2-Unity Barracuda and Windows Machine Learning (WinML)-using the inference time as the primary evaluation metric; (ii) to provide benchmark values for state-of-the-art DL models that can be integrated in different medical applications (e.g., Yolo and Unet models). In this study, we executed DL models with various complexities and analyzed inference times ranging from a few milliseconds to seconds. Our results show that Unity Barracuda is significantly faster than WinML (p-value < 0.005). With our findings, we sought to provide practical guidance and reference values for future studies aiming to develop single, portable AR systems for real-time medical assistance.

Detecting anatomical characteristics of single motor units by combining high density electromyography and ultrafast ultrasound: a simulation study.

Muscle force production is the result of a sequence of electromechanical events that translate the neural drive issued to the motor units (MUs) into tensile forces on the tendon. Current technology allows this phenomenon to be investigated non-invasively. Single MU excitation and its mechanical response can be studied through high-density surface electromyography (HDsEMG) and ultrafast ultrasound (US) imaging respectively. In this study, we propose a method to integrate these two techniques to identify anatomical characteristics of single MUs. Specifically, we tested two algorithms, combining the tissue velocity sequence (TVS, obtained from ultrafast US images), and the MU firings (extracted from HDsEMG decomposition). The first is the Spike Triggered Averaging (STA) of the TVS based on the occurrences of individual MU firings, while the second relies on the correlation between the MU firing patterns and the TVS spatio-temporal independent components (STICA). A simulation model of the muscle contraction was adapted to test the algorithms at different degrees of neural excitation (number of active MUs) and MU synchronization. The performances of the two algorithms were quantified through the comparison between the simulated and the estimated characteristics of MU territories (size, location). Results show that both approaches are negatively affected by the number of active MU and synchronization levels. However, STICA provides a more robust MU territory estimation, outperforming STA in all the tested conditions. Our results suggest that spatio-temporal independent component decomposition of TVS is a suitable approach for anatomical and mechanical characterization of single MUs using a combined HDsEMG and ultrafast US approach.