Design of an Inertial-Sensor-Based Data Glove for Hand Function Evaluation

Lin, Bor-Shing; Lee, I-Jung; Yang, Shuyu; Lo, Yi-Chiang; Lee, Junghsi; Chen, Jean-Lon

doi:10.3390/s18051545

Cited by 80 publications

(81 citation statements)

References 30 publications

(50 reference statements)

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“…The precision of the proposed methods under laboratory conditions is comparable to the precision reported in the literature. There are several previous contributions for IMU-based hand motion tracking that use customized rigid measurement platforms [24,25] or blocks of wood cut to specific angles [23] instead of considering measurements in human hands. Connolly et al perform experiments in patients but present only coefficients of variation based on video recordings [23].…”

Section: Discussionmentioning

confidence: 99%

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A Tangible Solution for Hand Motion Tracking in Clinical Applications

Salchow-Hömmen

Callies

Laidig

et al. 2019

Sensors

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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.

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Section: Discussionmentioning

confidence: 99%

“…Their evaluation lacks real-time measurements in realistic conditions and numeric results with human hands. Recently, Lin et al [25] presented a similar design. They report a mean error in joint angles under ±3° for a 15 min measurement of extension and flexion.…”

Section: Introductionmentioning

confidence: 99%

A Tangible Solution for Hand Motion Tracking in Clinical Applications

Salchow-Hömmen

Callies

Laidig

et al. 2019

Sensors

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“…Different kinds of sensors are currently employed in the design of data glove to provide high accuracy and to measure the activities of human fingers. Apart from designing low-cost data gloves, currently, researchers have focused on modular and expansible data glove design in order to improve adaptability [82]. Future research direction includes the addition of multiple feedback in a single finger of the data glove while ensuring consistent data across a large range of the hand.…”

Section: Discussionmentioning

confidence: 99%

“…A data glove can provide information about a human hand motion, hand pose and orientation in 3D space [80] and even force information [81]. The application of enhanced sensors such as Inertial measurement Unit has made it possible for other information of the human hand activities such as acceleration, angular velocity, and magnetic field to be measured using the data glove [82], [83]. Fang et al proposed a novel data glove which can capture human arm-hand motions simultaneously [84].…”

Section: ) Data Glovesmentioning

confidence: 99%

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Ogenyi

Liu

Yang

et al. 2021

IEEE Trans. Cybern.

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This paper presents a state-of-the-art survey on robotic systems, sensors, actuators and collaborative strategies for Physical Human-Robot Collaboration (pHRC). The paper starts with an overview of some robotic systems with cuttingedge technologies (sensors and actuators) suitable for pHRC operations and the intelligent assist devices employed in pHRC. Sensors being among the essential components to establish communication between a human and a robotic system are surveyed. The sensor supplies the signal needed to drive the robotic actuators. The survey reveals that the design of new generation collaborative robots and other intelligent robotic systems has paved the way for sophisticated learning techniques and control algorithms to be deployed in pHRC. Furthermore, it revealed relevant components needed to be considered for effective pHRC to be accomplished. Finally, a discussion of the major advances made, some research directions, and future challenges are presented.

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“…Complex forms of movement focus on localized body behavior. A BTS covers a range of positions for wearable devices according to behavioral characteristics, such as a head controller [91,92], smart gloves [93,94], an exoskeleton for the waist [95,96,97], and smart shoes [98,99]. In addition, smart textiles provide possibilities for behavior recognition.…”

Section: Systematic Analysis Of Framework Unitsmentioning

confidence: 99%

Systematic Analysis of a Military Wearable Device Based on a Multi-Level Fusion Framework: Research Directions

Shi

Zhang

Liu

et al. 2019

Sensors

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With the development of the Internet of Battlefield Things (IoBT), soldiers have become key nodes of information collection and resource control on the battlefield. It has become a trend to develop wearable devices with diverse functions for the military. However, although densely deployed wearable sensors provide a platform for comprehensively monitoring the status of soldiers, wearable technology based on multi-source fusion lacks a generalized research system to highlight the advantages of heterogeneous sensor networks and information fusion. Therefore, this paper proposes a multi-level fusion framework (MLFF) based on Body Sensor Networks (BSNs) of soldiers, and describes a model of the deployment of heterogeneous sensor networks. The proposed framework covers multiple types of information at a single node, including behaviors, physiology, emotions, fatigue, environments, and locations, so as to enable Soldier-BSNs to obtain sufficient evidence, decision-making ability, and information resilience under resource constraints. In addition, we systematically discuss the problems and solutions of each unit according to the frame structure to identify research directions for the development of wearable devices for the military.

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Design of an Inertial-Sensor-Based Data Glove for Hand Function Evaluation

Cited by 80 publications

References 30 publications

A Tangible Solution for Hand Motion Tracking in Clinical Applications

A Tangible Solution for Hand Motion Tracking in Clinical Applications

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Systematic Analysis of a Military Wearable Device Based on a Multi-Level Fusion Framework: Research Directions

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