An objective evaluation method for rehabilitation exergames

The aim of this work is to objectively evaluate the performance of patients using a virtual rehabilitation system called MIRA. MIRA is a software platform which converts conventional therapeutic exercises into games, enabling the user to practice the given exercise by playing a game. The system includes a motion sensor to track and capture user's movements. Our assessment of the performance quality is based on the recorded trajectories of the human skeleton joints. We employ two different machine learning approaches, dynamic time warping (DTW) and hidden Markov modeling (HMM), both widely used for gesture recognition, to compare the user's performance with that of a reference as ground truth

Rehabilitation Exergames: use of motion sensing and machine learning to quantify exercise performance in healthy volunteers

Background: Performing physiotherapy exercises in front of a physiotherapist yields qualitative assessment notes and immediate feedback. However, practicing the exercises at home lacks feedback on how well or not patients are performing the prescribed tasks. The absence of proper feedback might result in patients doing the exercises incorrectly, which could worsen their condition. Objective: We propose the use of two machine learning algorithms, namely Dynamic Time Warping (DTW) and Hidden Markov Model (HMM), to quantitively assess the patient’s performance with respects to a reference. Methods: Movement data were recorded using a Kinect depth sensor, capable of detecting 25 joints in the human skeleton model, and were compared to those of a reference. 16 participants were recruited to perform four different exercises: shoulder abduction, hip abduction, lunge, and sit-to-stand. Their performance was compared to that of a physiotherapist as a reference. Results: Both algorithms show a similar trend in assessing participants' performance. However, their sensitivity level was different. While DTW was more sensitive to small changes, HMM captured a general view of the performance, being less sensitive to the details. Conclusions: The chosen algorithms demonstrated their capacity to objectively assess physical therapy performances. HMM may be more suitable in the early stages of a physiotherapy program to capture and report general performance, whilst DTW could be used later on to focus on the detail

Augmented reality system for digital rectal examination training and assessment: system validation

Background: Digital rectal examination is a difficult examination to learn and teach because of limited opportunities for practice; however, the main challenge is that students and tutors cannot see the finger when it is palpating the anal canal and prostate gland inside the patients. Objective: This paper presents an augmented reality system to be used with benchtop models commonly available in medical schools with the aim of addressing the problem of lack of visualization. The system enables visualization of the examining finger, as well as of the internal organs when performing digital rectal examinations. Magnetic tracking sensors are used to track the movement of the finger, and a pressure sensor is used to monitor the applied pressure. By overlaying a virtual finger on the real finger and a virtual model on the benchtop model, students can see through the examination and finger maneuvers. Methods: The system was implemented in the Unity game engine (Unity Technologies) and uses a first-generation HoloLens (Microsoft Inc) as an augmented reality device. To evaluate the system, 19 participants (9 clinicians who routinely performed digital rectal examinations and 10 medical students) were asked to use the system and answer 12 questions regarding the usefulness of the system. Results: The system showed the movement of an examining finger in real time with a frame rate of 60 fps on the HoloLens and accurately aligned the virtual and real models with a mean error of 3.9 mm. Users found the movement of the finger was realistic (mean 3.9, SD 1.2); moreover, they found the visualization of the finger and internal organs were useful for teaching, learning, and assessment of digital rectal examinations (finger: mean 4.1, SD 1.1; organs: mean 4.6, SD 0.8), mainly targeting a novice group. Conclusions: The proposed augmented reality system was designed to improve teaching and learning of digital rectal examination skills by providing visualization of the finger and internal organs. The initial user study proved its applicability and usefulness

Data-driven texture modeling and rendering on electrovibration display

With the introduction of variable friction displays, new possibilities have emerged in haptic texture rendering on flat surfaces. In this work, we propose a data-driven method for realistic texture rendering on an electrovibration display. We first describe a motorized linear tribometer designed to collect lateral frictional forces from textured surfaces under various scanning velocities and normal forces. We then propose an inverse dynamics model of the display to describe its output-input relationship using nonlinear autoregressive neural networks with external input. Forces resulting from applying a pseudo-random binary signal to the display are used to train each network under the given experimental condition. In addition, we propose a two-step interpolation scheme to estimate actuation signals for arbitrary conditions under which no prior data have been collected. A comparison between real and virtual forces in the frequency domain shows promising results for recreating virtual textures similar to the real ones, also revealing the capabilities and limitations of the proposed method. We also conducted a human user study to compare the performance of our neural-network-based method with that of a record-and-playback method. The results showed that the similarity between the real and virtual textures generated by our approach was significantly higher

Augmented reality system for digital rectal examination training and assessment

Background: Digital Rectal Examination (DRE) is a difficult examination to learn and teach because of its unsighted nature and limited cases to practice. The main challenge is students and tutors cannot see the finger when it is palpating the anal canal and prostate gland inside the patients. Objective: This project presents an Augmented Reality (AR) system to be used with benchtop models commonly available in medical schools. It enables the visualisation of the examining finger, as well as the internal organs when performing DRE. Magnetic tracking sensors are used to track the movement of the finger and a pressure sensor to monitor the applied pressure. By overlaying a virtual finger on the real finger and a virtual model on the benchtop model, students can see-through the examination and finger manoeuvres. Methods: The system was implemented in Unity, and it uses a first-generation Microsoft HoloLens, as an augmented reality device. A user study was conducted to evaluate the system with 19 participants (9 clinicians who routinely perform DRE and 10 medical students). Once finished, they were asked to answer 12 questions regarding the usefulness of the system. Results: The system shows the movement of an examining finger in real-time with a frame rate of 60 FPS on the HoloLens. It can accurately align the virtual and real models with a mean error of 3.9mm. The user study suggests that the movement of the finger is realistic; moreover, the visualisation of the finger and internal organs are useful for teaching, learning, and assessment of DRE, mainly targeting a novice group. Conclusions: The proposed AR system is designed to improve the teaching of DRE skills by providing visualisation of the finger and internal organs. The initial user study proved its applicability and usefulness

Development and validation of a novel 3D-printed simulation model for open oesophageal atresia and tracheo-oesophageal fistula repair

Background The role of simulation training in paediatric surgery is expanding as more simulation devices are designed and validated. We aimed to conduct a training needs assessment of UK paediatric surgical trainees to prioritise procedures for simulation, and to validate a novel 3D-printed simulation model for oesophageal atresia and tracheo-oesophageal fistula (OA-TOF) repair. Methods A questionnaire was sent to UK trainee paediatric surgeons surveying the availability and utility of simulation. The operation ranked as most useful to simulate was OA-TOF repair. 3D-printing techniques were used to build an OA-TOF model. Content, face and construct validity was assessed by 40 paediatric surgeons of varying experience. Results Thirty-four paediatric surgeons completed the survey; 79% had access to surgical simulation at least monthly, and 47% had access to paediatric-specific resources. Perceived utility of simulation was 4.1/5. Validation of open OA-TOF repair was conducted by 40 surgeons. Participants rated the model as useful 4.9/5. Anatomical realism was scored 4.2/5 and surgical realism 3.9/5. The model was able to discriminate between experienced and inexperienced surgeons. Conclusion UK paediatric surgeons voted OA-TOF repair as the most useful procedure to simulate. In response we have developed and validated an affordable 3D-printed simulation model for open OA-TOF repair