Hand Motion Tracking System Research Articles

Design of a Wearable Real-Time Hand Motion Tracking System Using an Array of Soft Polymer Acoustic Waveguides.

Robust hand motion tracking holds promise for improved human-machine interaction in diverse fields, including virtual reality, and automated sign language translation. However, current wearable hand motion tracking approaches are typically limited in detection performance, wearability, and durability. This article presents a hand motion tracking system using multiple soft polymer acoustic waveguides (SPAWs). The innovative use of SPAWs as strain sensors offers several advantages that address the limitations. SPAWs are easily manufactured by casting a soft polymer shaped as a soft acoustic waveguide and containing a commercially available small ceramic piezoelectric transducer. When used as strain sensors, SPAWs demonstrate high stretchability (up to 100%), high linearity (R2 > 0.996 in all quasi-static, dynamic, and durability tensile tests), negligible hysteresis (<0.7410% under strain of up to 100%), excellent repeatability, and outstanding durability (up to 100,000 cycles). SPAWs also show high accuracy for continuous finger angle estimation (average root-mean-square errors [RMSE] <2.00°) at various flexion-extension speeds. Finally, a hand-tracking system is designed based on a SPAW array. An example application is developed to demonstrate the performance of SPAWs in real-time hand motion tracking in a three-dimensional (3D) virtual environment. To our knowledge, the system detailed in this article is the first to use soft acoustic waveguides to capture human motion. This work is part of an ongoing effort to develop soft sensors using both time and frequency domains, with the goal of extracting decoupled signals from simple sensing structures. As such, it represents a novel and promising path toward soft, simple, and wearable multimodal sensors.

Hand Pronation-Supination Movement as a Proxy for Remotely Monitoring Gait and Posture Stability in Parkinson's Disease.

The Unified Parkinson’s Disease Rating Scale (UPDRS) is a subjective Parkinson’s Disease (PD) physician scoring/monitoring system. To date, there is no single upper limb wearable/non-contact system that can be used objectively to assess all UPDRS-III motor system subgroups (i.e., tremor (T), rigidity (R), bradykinesia (B), gait and posture (GP), and bulbar anomalies (BA)). We evaluated the use of a non-contact hand motion tracking system for potential extraction of GP information using forearm pronation–supination (P/S) motion parameters (speed, acceleration, and frequency). Twenty-four patients with idiopathic PD participated, and their UPDRS data were recorded bilaterally by physicians. Pearson’s correlation, regression analyses, and Monte Carlo validation was conducted for all combinations of UPDRS subgroups versus motion parameters. In the 262,125 regression models that were trained and tested, the models within 1% of the lowest error showed that the frequency of P/S contributes to approximately one third of all models; while speed and acceleration also contribute significantly to the prediction of GP from the left-hand motion of right handed patients. In short, the P/S better indicated GP when performed with the non-dominant hand. There was also a significant negative correlation (with medium to large effect size, range: 0.3–0.58) between the P/S speed and the single BA score for both forearms and combined UPDRS score for the dominant hand. This study highlights the potential use of wearable or non-contact systems for forearm P/S to remotely monitor and predict the GP information in PD.

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.

The Co-design of Hand Rehabilitation Exercises for Multiple Sclerosis Using Hand Tracking System.

Multiple sclerosis (MS) often affects motor function, leading to an adverse effect on daily living. Rehabilitation is important in terms of improving mobility and activities of daily living. Virtual environments (VE) are increasing in popularity within this research area, but research in terms of VE is still rare, for both the upper and lower limb, in people with MS. Leap Motion (LM), a hand motion tracking system, has demonstrated success in stroke research but has yet to be investigated within MS. Following a co-design approach, five participants with MS discussed in a focus group (FG) their hand mobility issues, their thoughts about this technology-based rehabilitation and motivational factors. Findings were incorporated into the design of a series of gamified upper limb rehabilitation exercises, using LM, on Unity Game Engine. Three participants returned and engaged in user testing session and a FG in order to evaluate and discuss their experience. Overall participants found the proposed technology-based exercises to be engaging, immersive and a desirable approach to rehabilitation. Participant feedback underlined the usefulness of co-creation, especially in accommodating the range of motivators and user preferences. However, the study highlighted the loss of tracking of hand movements with LM as one of the limitations. Participants stated they would be likely to use this approach at home if there was a definite rehabilitation benefit and related more to visualising which muscle groups or actions they were aiming to improve.

Magnetic hand motion tracking system for human–machine interaction

A magnetic hand motion tracking system for a variety of applications requiring human-machine interaction is presented. Small permanent magnets are fixed on fingernails as signal sources. A series of magnetic sensors on a wristband measure magnetic signals related to the movements of the fingertips. The measured data are transmitted to the application wirelessly. A hand geometric model and a least-square source localisation algorithm are utilised to calculate the hand postures. This system does not require wire connections and can be used conveniently and unobstructively.

An intelligent visual hand motion tracking system for home-based rehabilitation

Human motion tracking is the key technology for intelligent rehabilitation systems in which the movement of disabled people can be localised and learned and any undesired motion behaviour can be corrected to reach an expectation. This paper introduces a real-time tracking system of human hand motion, specifically used for home based rehabilitation. A hybrid tracking algorithm based on an improved Camshift and a Kalman filter is proposed. In the tracking process, Camshift converts the raw image to a flesh colour probability distribution image via a colour probability table. Considering the hand motion and flesh colour probability distribution, the size and location of a search window are initialised by using the object histogram model. Computed information is used to adjust current search window's size and location, then the location of the hand are found. Experimental results show that this algorithm is fast, accurate, and robust. It can not only deal with the skin colour interference problems, but also handle complex background.