Assessment of hand kinematics using inertial and magnetic sensors

Background:\ud Assessment of hand kinematics is important when evaluating hand functioning. Major drawbacks ofcurrent sensing glove systems are lack of rotational observability in particular directions, labourintensive calibration methods which are sensitive to wear and lack of an absolute hand orientationestimate.\ud \ud Methods:\ud We propose an ambulatory system using inertial sensors that can be placed on the hand, fingers andthumb. It allows a full 3D reconstruction of all finger and thumb joints as well as the absoluteorientation of the hand. The system was experimentally evaluated for the static accuracy, dynamicrange and repeatability.\ud \ud Results:\ud The RMS position norm difference of the fingertip compared to an optical system was 5±0.5 mm(mean ± standard deviation) for flexion-extension and 12.4±3.0 mm for combined flexion-extensionabduction-adduction movements of the index finger. The difference between index and thumb tipsduring a pinching movement was 6.5±2.1 mm. The dynamic range of the sensing system and filterwas adequate to reconstruct full 80 degrees movements of the index finger performed at 116 timesper minute, which was limited by the range of the gyroscope. Finally, the reliability study showed amean range difference over five subjects of 1.1±0.4 degrees for a flat hand test and1.8±0.6 degrees for a plastic mold clenching test, which is smaller than other reported data gloves.\ud \ud Conclusion:\ud Compared to existing data gloves, this research showed that inertial and magnetic sensors are of interest for ambulatory analysis of the human hand and finger kinematics in terms of static accuracy, dynamic range and repeatability. It allows for estimation of multi-degree of freedom joint movements using low-cost sensors

Low power glove for hand functioning analysis in children with cerebral palsy

In this paper, a low-cost glove has been manufactured to monitor and analyse the hand motion for the children who suffer from the cerebral palsy. Cerebral palsy (CP) is a combination of continual disorders affect the movement’s evolution due to a non-gradual disturbance in developing fetal or infant cerebrum. An Arduino Nano microcontroller with flex and force sensors are attached to soft cloth glove to form the analysis glove. The data of this study is collected from children who have cerebral palsy, non-cerebral palsy, and children who are treating by physiotherapy and then compared with each other. The results show that the analysis glove helps the physiotherapist to assess the hand functioning problem such as difficulty in hand grip and inability to fully bend the hand figures in general and thumb figure in particular. These remarks can help physiotherapists to define the required program to improve these functions and indications

A Tangible Solution for Hand Motion Tracking in Clinical Applications

Objective real-time assessment of hand motion is crucial in many clinical applications including technically-assisted physical rehabilitation of the upper extremity. We propose an inertial-sensor-based hand motion tracking system and a set of dual-quaternion-based methods for estimation of finger segment orientations and fingertip positions. The proposed system addresses the specific requirements of clinical applications in two ways: (1) In contrast to glove-based approaches, the proposed solution maintains the sense of touch. (2) In contrast to previous work, the proposed methods avoid the use of complex calibration procedures, which means that they are suitable for patients with severe motor impairment of the hand. To overcome the limited significance of validation in lab environments with homogeneous magnetic fields, we validate the proposed system using functional hand motions in the presence of severe magnetic disturbances as they appear in realistic clinical settings. We show that standard sensor fusion methods that rely on magnetometer readings may perform well in perfect laboratory environments but can lead to more than 15 cm root-mean-square error for the fingertip distances in realistic environments, while our advanced method yields root-mean-square errors below 2 cm for all performed motions.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Glove-based systems for medical applications: review of recent advancements

Human hand motion analysis is attracting researchers in the areas of neuroscience, biomedical engineering, robotics, human-machines interfaces (HMI), human-computer interaction (HCI), and artificial intelligence (AI). Among the others, the fields of medical rehabilitation and physiological assessments are suggesting high impact applications for wearable sensing systems. Glove-based systems are one of the most significant devices in assessing quantities related to hand movements. This paper provides updated survey among the main glove solutions proposed in literature for hand rehabilitation. Then, the process for designing glove-based systems is defined, by including all relevant design issues for researchers and makers. The main goal of the paper is to describe the basics of glove-based systems and to outline their potentialities and limitations. At the same time, roadmap to design and prototype the next generation of these devices is defined, according to the results of previous experiences in the scientific community

Applications of the PowerGlove

The hand is important in many daily life activities. During aging, quality of fine motor control of hand and fingers is decreasing. Also motor symptoms of the hand are important to define for instance the neurological state of a Parkinson’s disease patient. Although objective and reliable measurement of hand and finger dynamics is of interest, current measurement systems are limited. This paper describes the application of the PowerGlove, a new measurement system based on miniature inertial and magnetic sensors, to study the finger interdependency in healthy elderly and objectively quantify hand motor symptoms in Parkinson’s disease. Results of pilot experiments in young healthy subjects are shown to evaluate the feasibility of the applications

Accuracy and repeatability of wrist joint angles in boxing using an electromagnetic tracking system

© 2019, The Author(s). The hand-wrist region is reported as the most common injury site in boxing. Boxers are at risk due to the amount of wrist motions when impacting training equipment or their opponents, yet we know relatively little about these motions. This paper describes a new method for quantifying wrist motion in boxing using an electromagnetic tracking system. Surrogate testing procedure utilising a polyamide hand and forearm shape, and in vivo testing procedure utilising 29 elite boxers, were used to assess the accuracy and repeatability of the system. 2D kinematic analysis was used to calculate wrist angles using photogrammetry, whilst the data from the electromagnetic tracking system was processed with visual 3D software. The electromagnetic tracking system agreed with the video-based system (paired t tests) in both the surrogate ( 0.9). In the punch testing, for both repeated jab and hook shots, the electromagnetic tracking system showed good reliability (ICCs > 0.8) and substantial reliability (ICCs > 0.6) for flexion–extension and radial-ulnar deviation angles, respectively. The results indicate that wrist kinematics during punching activities can be measured using an electromagnetic tracking system