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.

Background: Hospitalized older adults often spend prolonged periods of time bedridden, leading to decreased muscle strength and function. To tackle this, rehabilitation aims to keep patients active and train affected muscles. Exergames have proven to be effective in the rehabilitation of different patient populations and offer a motivating solution to combat inactivity associated with hospitalization. Furthermore, blood flow restriction (BFR) is effective in therapy for weakened patients, so combining BFR and exergames might be promising. Objective: As part of an iterative process of user-centered development, this mixed method study investigates the acceptability and feasibility of the Ghostly game as a stand-alone added therapy or combined with BFR in strength training of hospitalized older adults. Methods: A mixed methods study was conducted on 15 hospitalized older adults. Participants were randomized into 3 groups and received daily interventions from the moment they were included in the geriatric ward, until discharge from the hospital. The Ghostly group received daily conventional therapy with the Ghostly game as added therapy, the Ghostly + BFR group received daily conventional therapy with Ghostly in combination with BFR as added therapy and last, the control group received daily conventional therapy with dose-matched isometric exercises as added therapy. The primary outcome, user experience, was assessed before discharge from the hospital using the Usefulness, Satisfaction, and Ease of Use questionnaire and through expert observations. Clinical outcomes such as muscle strength, muscle architecture, and segmental body composition were assessed at baseline and before discharge from the hospital to test the feasibility of the research protocol in preparation for future randomized controlled trials. Results: A total of 15 hospitalized older adults (11 female participants, 73.33%) were included in this study with an average age of 84.53 (range: 78‐94) years. Participants received an average of 3.47 (range: 3‐5) intervention sessions after transferring to the geriatric ward of the hospital. Results on user experience revealed high scores on all subcategories of the Usefulness, Satisfaction, and Ease of Use questionnaire (usefulness: 78.93%, ease of use: 82.99%, ease of learning: 85.36%, and satisfaction: 87.55%). Furthermore, expert observations identified issues with color contrast, reaction time speed, and the need to tailor the game to accommodate the diverse requirements of different patient populations. All outcomes and procedures were found feasible for a future randomized controlled trial. Conclusions: This mixed methods study combines the innovative aspects of an electromyography-driven exergame with strength training principles of BFR and reveals the acceptability and feasibility of the Ghostly game as a stand-alone added therapy modality for strength training in hospitalized older adults and in combination with BFR. Future improvements of the exergame could focus on addressing expert-identified issues, including optimizing color contrast, adjusting reaction time speeds, and tailoring the game to meet the needs of different patient populations.