Posts Tagged Leap Motion Controller
[Abstract] Hand Rehabilitation via Gesture Recognition Using Leap Motion Controller – Conference Paper
Nowadays, a stroke is the fourth leading cause of death in the United States. In fact, every 40 seconds, someone in the US is having a stroke. Moreover, around 50% of stroke survivors suffer damage to the upper extremity –. Many actions of treating and recovering from a stroke have been developed over the years, but recent studies show that combining the recovery process with the existing rehabilitation plan provides better results and a raise in the patients quality of life –. Part of the stroke recovery process is a rehabilitation plan . The process can be difficult, intensive and long depending on how adverse the stroke and which parts of the brain were damaged. These processes usually involve working with a team of health care providers in a full extensive rehabilitation plan, which includes hospital care and home exercises.
Hand rehabilitation is fundamental after stroke or surgery. Traditional rehabilitation requires a therapist and implies high costs, stress for the patient, and subjective evaluation of the therapy effectiveness. Alternative approaches, based on mechanical and tracking-based gloves, can be really effective when used in virtual reality (VR) environments. Mechanical devices are often expensive, cumbersome, patient specific and hand specific, while tracking-based devices are not affected by these limitations but, especially if based on a single tracking sensor, could suffer from occlusions. In this paper, the implementation of a multi-sensors approach, the Virtual Glove (VG), based on the simultaneous use of two orthogonal LEAP motion controllers, is described. The VG is calibrated and static positioning measurements are compared with those collected with an accurate spatial positioning system. The positioning error is lower than 6 mm in a cylindrical region of interest of radius 10 cm and height 21 cm. Real-time hand tracking measurements are also performed, analysed and reported. Hand tracking measurements show that VG operated in real-time (60 fps), reduced occlusions, and managed two LEAP sensors correctly, without any temporal and spatial discontinuity when skipping from one sensor to the other. A video demonstrating the good performance of VG is also collected and presented in the Supplementary Materials. Results are promising but further work must be done to allow the calculation of the forces exerted by each finger when constrained by mechanical tools (e.g., peg-boards) and for reducing occlusions when grasping these tools. Although the VG is proposed for rehabilitation purposes, it could also be used for tele-operation of tools and robots, and for other VR applications.
Hand rehabilitation is extremely important for recovering from post-stroke or post-surgery residual impairments and its effectiveness depends on frequency, duration and quality of the rehabilitation sessions . Traditional rehabilitation requires a therapist for driving and controlling patients during sessions. Procedure effectiveness is evaluated subjectively by the therapist, basing on experience. In the last years, several automated (tele)rehabilitation gloves, based on mechanical devices or tracking sensors, have been presented [2,3,4,5,6,7,8,9,10]. These gloves allow the execution of therapy at home and rehabilitation effectiveness can be analytically calculated and summarized in numerical parameters, controlled by therapists through Internet. Moreover, these equipment can be easily interfaced with virtual reality (VR) environments , which have been proven to increase rehabilitation efficacy . Mechanical devices are equipped with pressure sensors and pneumatic actuators for assisting and monitoring the hand movements and for applying forces to which the patient has to oppose [13,14]. However, they are expensive, cumbersome, patient specific (different patients cannot reuse the same system) and hand specific (the patient cannot use the same system indifferently with both hands). Tracking-based gloves consist of computer vision algorithms for the analysis and interpretation of videos from depth sensing sensors to calculate hand kinematics in real time [10,15,16,17,18,19]. Besides depth sensors, LEAP  is a small and low-cost hand 3D tracking device characterized by high-resolution and high-reactivity [21,22,23], used in VR , and has been recently presented and tested with success in the hand rehabilitation, with exercises designed in VR environments . Despite the advantages of using LEAP with VR, a single sensor does not allow accurate quantitative evaluation of hand and fingers tracking in case of occlusions. The system proposed in  consisted on two orthogonal LEAPs designed to reduce occlusions and to improve objective hand-tracking evaluation. The two sensors were fixed to a wood support that maintained them orthogonal each other. The previous prototype was useful to test the robustness of each sensor, in presence of the other, to the potential infra-red interferences, to evaluate the maintenance of the maximum operative range of each sensor and, finally, to demonstrate the hand tracking idea. However, it was imprecise, due to the usage of raw VG support and positioning system, the non-optimal reciprocal positioning of the sensors, and the impossibility of performing a reciprocal calibration independent of the sensors measurements. This fact did not allow the evaluation of the intrinsic precision of the VG and to perform accurate, real-time quantitative hand tracking measurements. In this paper, we present a method for constructing an engineered version of the LEAP based VG, a technique for its accurate calibration and for collecting accurate positioning measurements and high-quality evaluation of positioning errors, specific of VG. Moreover, real-time experimental hand tracking measurements were collected (a video demonstrating its real-time performance and precision was also provided in the Supplementary Materials), presented and discussed.[…]
[Abstract & References] A human–computer interface for wrist rehabilitation: a pilot study using commercial sensors to detect wrist movements
Health conditions might cause muscle weakness and immobility in some body parts; hence, physiotherapy exercises play a key role in the rehabilitation. To improve the engagement during the rehabilitation process, we therefore propose a human–computer interface (serious game) in which five wrist movements (extension, flexion, pronation, supination and neutral) are detected via two commercial sensors (Leap motion controller and Myo armband). Leap motion provides data regarding positions of user’s finger phalanges through two infrared cameras, while Myo armband facilitates electromyography signal and inertial motion of user’s arm through its electrodes and inertial measurement unit. The main aim of this study is to explore the performance of these sensors on wrist movement recognition in terms of accuracy, sensitivity and specificity. Eight healthy participants played 5 times a proposed game with each sensor in one session. Both sensors reported over 85% average recognition accuracy in the five wrist movements. Based on t test and Wilcoxon signed-rank test, early results show that there were significant differences between Leap motion controller and Myo armband recognitions in terms of average sensitivities on extension (), flexion () and pronation () movements, and average specificities on extension () and pronation () movements.
[ARTICLE] A Review on Technical and Clinical Impact of Microsoft Kinect on Physical Therapy and Rehabilitation – Full Text HTML
This paper reviews technical and clinical impact of the Microsoft Kinect in physical therapy and rehabilitation. It covers the studies on patients with neurological disorders including stroke, Parkinson’s, cerebral palsy, and MS as well as the elderly patients. Search results in Pubmed and Google scholar reveal increasing interest in using Kinect in medical application. Relevant papers are reviewed and divided into three groups: (1) papers which evaluated Kinect’s accuracy and reliability, (2) papers which used Kinect for a rehabilitation system and provided clinical evaluation involving patients, and (3) papers which proposed a Kinect-based system for rehabilitation but fell short of providing clinical validation. At last, to serve as technical comparison to help future rehabilitation design other sensors similar to Kinect are reviewed.
Traditionally, a great portion of physical therapy and rehabilitation assessment of stroke patients is based on a therapist’s observation and judgment. The assessments methods (e.g., Fugl-Meyer et al. Assessment of Physical Performance ) rely heavily on the therapists visual assessment of how the patient is performing a standard task. This process needs a trained Physical Therapist (PT) or Occupational Therapist (OCT) to spend one on one time with the patient. Yet, the assessment can be inaccurate for several reasons one of which is the subjectivity of these behavioral and clinical assessments. Sensor and computing technology that can be used for motion capture have advanced drastically in the past few years; they have become more capable and affordable. Motion capture systems (MoCap) record human body’s kinematic data with high accuracy and reliability; analysis of MoCap data results in better clinical and behavioral assessment and more efficient therapeutic decision making accordingly.