A novel tool for characterising upper limb function in progressive multiple sclerosis through kinematic assessment.

INTRODUCTION: Current upper limb assessment methods in MS rely on measuring duration in tasks like the nine-hole peg test (9HPT). Kinematic techniques may provide a more useful measure of functional change in clinical and research practice. The aim of this study was to assess upper limb function prospectively in people with progressive MS using a kinematic 3D motion capture system and compare with current measures. METHODS: 42 people with progressive MS (PwPMS) and 15 healthy controls reached-and-grasped different objects whilst recorded by a kinematic assessment system. 9HPT, Expanded Disability Status Scale (EDSS), and patient reported outcome measures (PROs) were collected. All measures were taken at baseline for PwPMS and controls, and again at six months for PwPMS. RESULTS: Relative to controls, PwPMS had significantly longer reaction (0.11 s, p < 0.05) and reach (0.25 s, p < 0.05) times. PwPMS took longer to pick-up (0.34 s, p < 0.05), move (0.14 s, p < 0.05), and place (0.18 s, p < 0.05) objects. PwPMS had lower peak velocities when reaching (7.4 cm/s, p < 0.05) and moving (7.3 cm/s, p < 0.05) objects. Kinematic assessment demonstrated consistent differences between PwPMS with mild and severe upper limb dysfunction as defined by PROs, which were not captured by 9HPT or EDSS in this group. PwPMS demonstrated altered grip apertures profiles, as measured by their ability to complete individual parts of the reach and grasp task, between the baseline and follow-up timepoints. CONCLUSIONS: We have created and tested a novel upper limb function assessment tool which has detected changes and characteristics in hand function, not currently captured by the EDSS and 9HPT.

Cardiac X-ray image-based haptic guidance for robot-assisted coronary intervention: a feasibility study.

PURPOSE: Effective and efficient haptic guidance is desirable for tele-operated robotic surgery because it has a potential to enhance surgeon's skills, especially in coronary interventions where surgeon loses both an eye-hand coordination and a direct sight to the organ. This paper proposes a novel haptic guidance procedure-both kinesthetic and cutaneous, which solely depends upon X-ray images, for tele-robotic system that assists an efficient navigation of the guidewire towards the target location during a coronary intervention. METHODS: Proposed methodology requires cardiologists to draw virtual fixtures (VFs) on angiograms as a preoperative procedure. During an operation, these VFs direct the guidewire to the desired coronary vessel. For this, the position and orientation of guidewire tip are calculated with respect to VFs' anatomy, using image processing on the real-time 2D fluoroscopic images. The haptic feedbacks are then rendered on to the master device depending on the interaction with attractive and repulsive, guidance and forbidden region VFs. RESULTS: A feasibility study in the laboratory environment is performed by using a webcam as an image acquisition device and a phantom-based coronary vessel model. The subsequent statistical analysis shows that, on an average, a decrease of approx. 37% in task completion time is observed with haptic feedback. Moreover, haptic guidance is found effective for most difficult branch, whereas there is a minimal significance of such haptics for the easiest branch. CONCLUSIONS: The proposed haptic guidance procedure may assist cardiologists for an efficient and effective guidewire navigation during a surgical procedure. The cutaneous haptics (vibration feedback) is found more helpful in coronary interventions compared with kinesthetic haptics (force feedback).

The discerning eye of computer vision: Can it measure Parkinson's finger tap bradykinesia?

OBJECTIVE: The worldwide prevalence of Parkinson's disease is increasing. There is urgent need for new tools to objectively measure the condition. Existing methods to record the cardinal motor feature of the condition, bradykinesia, using wearable sensors or smartphone apps have not reached large-scale, routine use. We evaluate new computer vision (artificial intelligence) technology, DeepLabCut, as a contactless method to quantify measures related to Parkinson's bradykinesia from smartphone videos of finger tapping. METHODS: Standard smartphone video recordings of 133 hands performing finger tapping (39 idiopathic Parkinson's patients and 30 controls) were tracked on a frame-by-frame basis with DeepLabCut. Objective computer measures of tapping speed, amplitude and rhythm were correlated with clinical ratings made by 22 movement disorder neurologists using the Modified Bradykinesia Rating Scale (MBRS) and Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). RESULTS: DeepLabCut reliably tracked and measured finger tapping in standard smartphone video. Computer measures correlated well with clinical ratings of bradykinesia (Spearman coefficients): -0.74 speed, 0.66 amplitude, -0.65 rhythm for MBRS; -0.56 speed, 0.61 amplitude, -0.50 rhythm for MDS-UPDRS; -0.69 combined for MDS-UPDRS. All p < .001. CONCLUSION: New computer vision software, DeepLabCut, can quantify three measures related to Parkinson's bradykinesia from smartphone videos of finger tapping. Objective 'contactless' measures of standard clinical examinations were not previously possible with wearable sensors (accelerometers, gyroscopes, infrared markers). DeepLabCut requires only conventional video recording of clinical examination and is entirely 'contactless'. This next generation technology holds potential for Parkinson's and other neurological disorders with altered movements.