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1. Developing a Patient-Centered Virtual Reality Healthcare System To Prevent the Onset of Delirium in ICU Patients

   https://doi.org/10.1109/SeGAH.2019.8882442  

   Suvajdzic, Marko; Bihorac, Azra; Rashidi, Parisa; Ruppert, Matthew; Williams, Seth; Ozrazgat-Baslanti, Tezcan; Ong, Triton; Appelbaum, Joel (August 2019, Developing a Patient-Centered Virtual Reality Healthcare System To Prevent the Onset of Delirium in ICU Patients)

   The purpose of the DREAMS project (DREAMS = Digital Rehabilitation Environment-Augmenting Medical System) is to research the feasibility and clinical potential of a virtual reality (VR) system for reducing the occurrence of delirium among patients in the intensive care unit (ICU). Preliminary results of this ongoing study show VR produces minimal clinical effects but are strongly enjoyed by patients and easy to administer. We discuss important lessons learned from applying VR in the ICU. 

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2. NeuroFlex: Feasibility of EEG-Based Motor Imagery Control of a Soft Glove for Hand Rehabilitation

   https://doi.org/10.3390/s25030610  

   Zare, Soroush; Beaber, Sameh I; Sun, Ye (February 2025, Sensors)

   Motor impairments resulting from neurological disorders, such as strokes or spinal cord injuries, often impair hand and finger mobility, restricting a person’s ability to grasp and perform fine motor tasks. Brain plasticity refers to the inherent capability of the central nervous system to functionally and structurally reorganize itself in response to stimulation, which underpins rehabilitation from brain injuries or strokes. Linking voluntary cortical activity with corresponding motor execution has been identified as effective in promoting adaptive plasticity. This study introduces NeuroFlex, a motion-intent-controlled soft robotic glove for hand rehabilitation. NeuroFlex utilizes a transformer-based deep learning (DL) architecture to decode motion intent from motor imagery (MI) EEG data and translate it into control inputs for the assistive glove. The glove’s soft, lightweight, and flexible design enables users to perform rehabilitation exercises involving fist formation and grasping movements, aligning with natural hand functions for fine motor practices. The results show that the accuracy of decoding the intent of fingers making a fist from MI EEG can reach up to 85.3%, with an average AUC of 0.88. NeuroFlex demonstrates the feasibility of detecting and assisting the patient’s attempted movements using pure thinking through a non-intrusive brain–computer interface (BCI). This EEG-based soft glove aims to enhance the effectiveness and user experience of rehabilitation protocols, providing the possibility of extending therapeutic opportunities outside clinical settings. 

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3. PrimSeq: A deep learning-based pipeline to quantitate rehabilitation training

   https://doi.org/10.1371/journal.pdig.0000044  

   Parnandi, Avinash; Kaku, Aakash; Venkatesan, Anita; Pandit, Natasha; Wirtanen, Audre; Rajamohan, Haresh; Venkataramanan, Kannan; Nilsen, Dawn; Fernandez-Granda, Carlos; Schambra, Heidi (June 2022, PLOS Digital Health)

   Chua Chin Heng, Matthew (Ed.)

   Stroke rehabilitation seeks to accelerate motor recovery by training functional activities, but may have minimal impact because of insufficient training doses. In animals, training hundreds of functional motions in the first weeks after stroke can substantially boost upper extremity recovery. The optimal quantity of functional motions to boost recovery in humans is currently unknown, however, because no practical tools exist to measure them during rehabilitation training. Here, we present PrimSeq, a pipeline to classify and count functional motions trained in stroke rehabilitation. Our approach integrates wearable sensors to capture upper-body motion, a deep learning model to predict motion sequences, and an algorithm to tally motions. The trained model accurately decomposes rehabilitation activities into elemental functional motions, outperforming competitive machine learning methods. PrimSeq furthermore quantifies these motions at a fraction of the time and labor costs of human experts. We demonstrate the capabilities of PrimSeq in previously unseen stroke patients with a range of upper extremity motor impairment. We expect that our methodological advances will support the rigorous measurement required for quantitative dosing trials in stroke rehabilitation. 

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4. Scientific basis and active ingredients of current therapeutic interventions for stroke rehabilitation

   https://doi.org/10.3233/RNN-211243  

   Ranganathan, Rajiv; Doherty, Carson; Gussert, Michael; Kaplinski, Eva; Koje, Mary; Krishnan, Chandramouli (June 2022, Restorative Neurology and Neuroscience)

   Background: Despite tremendous advances in the treatment and management of stroke, restoring motor and functional outcomes after stroke continues to be a major clinical challenge. Given the wide range of approaches used in motor rehabilitation, several commentaries have highlighted the lack of a clear scientific basis for different interventions as one critical factor that has led to suboptimal study outcomes. Objective: To understand the content of current therapeutic interventions in terms of their active ingredients. Methods: We conducted an analysis of randomized controlled trials in stroke rehabilitation over a 2-year period from 2019-2020. Results: There were three primary findings: (i) consistent with prior reports, most studies did not provide an explicit rationale for why the treatment would be expected to work, (ii) most therapeutic interventions mentioned multiple active ingredients and there was not a close correspondence between the active ingredients mentioned versus the active ingredients measured in the study, and (iii) multimodal approaches that involved more than one therapeutic approach tended to be combined in an ad-hoc fashion, indicating the lack of a targeted approach. Conclusion: These results highlight the need for strengthening cross-disciplinary connections between basic science and clinical studies, and the need for structured development and testing of therapeutic approaches to find more effective treatment interventions. 

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5. MARS: mmWave-based Assistive Rehabilitation System for Smart Healthcare

   https://doi.org/10.1145/3477003  

   An, Sizhe; Ogras, Umit Y. (October 2021, ACM Transactions on Embedded Computing Systems)

   Rehabilitation is a crucial process for patients suffering from motor disorders. The current practice is performing rehabilitation exercises under clinical expert supervision. New approaches are needed to allow patients to perform prescribed exercises at their homes and alleviate commuting requirements, expert shortages, and healthcare costs. Human joint estimation is a substantial component of these programs since it offers valuable visualization and feedback based on body movements. Camera-based systems have been popular for capturing joint motion. However, they have high-cost, raise serious privacy concerns, and require strict lighting and placement settings. We propose a millimeter-wave (mmWave)-based assistive rehabilitation system (MARS) for motor disorders to address these challenges. MARS provides a low-cost solution with a competitive object localization and detection accuracy. It first maps the 5D time-series point cloud from mmWave to a lower dimension. Then, it uses a convolution neural network (CNN) to estimate the accurate location of human joints. MARS can reconstruct 19 human joints and their skeleton from the point cloud generated by mmWave radar. We evaluate MARS using ten specific rehabilitation movements performed by four human subjects involving all body parts and obtain an average mean absolute error of 5.87 cm for all joint positions. To the best of our knowledge, this is the first rehabilitation movements dataset using mmWave point cloud. MARS is evaluated on the Nvidia Jetson Xavier-NX board. Model inference takes only 64 s and consumes 442 J energy. These results demonstrate the practicality of MARS on low-power edge devices. 

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