Telerehabilitation is a cost-effective alternative to in-clinic rehabilitation. Although convenient, it lacks immersive and free-viewpoint patient visualization. Current research explores two solutions to this issue. Mesh-based methods use 3D models and motion capture for AR visualization. However, they are labor-intensive and less photorealistic than 2D images. Microsoft's Holoportation generates photorealistic 3D models with eight RGBD cameras in real time. However, it requires complex setups, high GPU power, and high-speed communication infrastructure, making deployment challenging. This article presents a Real-Time Free-Viewpoint Holographic Patient Rendering (RT-FVHP) system for telerehabilitation. Unlike traditional methods that require manually crafted assets such as 3D meshes, texture maps, and skeletal rigging, our data-driven approach eliminates the need for explicit asset definitions. Inspired by the HumanNeRF framework, we retarget dynamic human poses to a canonical pose and leverage 3D Gaussian Splatting to train a neural network in canonical space for patient representation. The trained model generates 2D RGB$\sigma$σ outputs via Gaussian Splatting rasterization, guided by camera parameters and human pose inputs. Compatible with HoloLens 2 and web-based platforms, RT-FVHP operates effectively under real-world conditions, including handling occlusions caused by treadmills. Occlusion handling is accomplished using our Shape-Enforced Gaussian Density Control (SGDC), which initializes and densifies 3D Gaussians in occluded regions using estimated SMPL human body priors. This approach minimizes manual intervention while ensuring complete body reconstruction. With efficient Gaussian rasterization, the model delivers real-time performance of up to 400 FPS at 1080p resolution on a dedicated RTX6000 GPU.

Stroke is the leading long-term disability and causes a significant financial burden associated with rehabilitation. In poststroke rehabilitation, individuals with hemiparesis have a specialized demand for coordinated movement between the paretic and the nonparetic legs. The split-belt treadmill can effectively facilitate the paretic leg by slowing down the belt speed for that leg while the patient is walking on a split-belt treadmill. Although studies have found that split-belt treadmills can produce better gait recovery outcomes than traditional single-belt treadmills, the high cost of split-belt treadmills is a significant barrier to stroke rehabilitation in clinics. In this article, we design an AI-based system for the single-belt treadmill to make it act like a split-belt by adjusting the belt speed instantaneously according to the patient's microgait phases. This system only requires a low-cost RGB camera to capture human gait patterns. A novel microgait classification pipeline model is used to detect gait phases in real time. The pipeline is based on self-supervised learning that can calibrate the anchor video with the real-time video. We then use a ResNet-LSTM module to handle temporal information and increase accuracy. A real-time filtering algorithm is used to smoothen the treadmill control. We have tested the developed system with 34 healthy individuals and four stroke patients. The results show that our system is able to detect the gait microphase accurately and requires less human annotation in training, compared to the ResNet50 classifier. Our system "Splicer" is boosted by AI modules and performs comparably as a split-belt system, in terms of timely varying left/right foot speed, creating a hemiparetic gait in healthy individuals, and promoting paretic side symmetry in force exertion for stroke patients. This innovative design can potentially provide cost-effective rehabilitation treatment for hemiparetic patients.