A Tangible Solution for Hand Motion Tracking in Clinical Applications

Salchow-Hömmen, Christina; Callies, Leonie; Laidig, Daniel; Valtin, Markus; Schauer, Thomas; Seel, Thomas

doi:10.3390/s19010208

Cited by 43 publications

(69 citation statements)

References 50 publications

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“…Online Applications include real-time motion tracking for wearable biofeedback systems [22] as well as robotic and neuroprosthetic motion support systems [23]. In state-of-the-art solutions, the user first performs an initial calibration procedure before the sensor system is connected to an assistive device that uses the measurements to provide e.g., biofeedback or motion support.…”

Section: Arbitrary-motionsmentioning

confidence: 99%

Robust Plug-and-Play Joint Axis Estimation Using Inertial Sensors

Olsson

Kok

Seel

et al. 2020

Sensors

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Inertial motion capture relies on accurate sensor-to-segment calibration. When two segments are connected by a hinge joint, for example in human knee or finger joints as well as in many robotic limbs, then the joint axis vector must be identified in the intrinsic sensor coordinate systems. Methods for estimating the joint axis using accelerations and angular rates of arbitrary motion have been proposed, but the user must perform sufficiently informative motion in a predefined initial time window to accomplish complete identifiability. Another drawback of state of the art methods is that the user has no way of knowing if the calibration was successful or not. To achieve plug-and-play calibration, it is therefore important that 1) sufficiently informative data can be extracted even if large portions of the data set consist of non-informative motions, and 2) the user knows when the calibration has reached a sufficient level of accuracy. In the current paper, we propose a novel method that achieves both of these goals. The method combines acceleration- and angular rate information and finds a globally optimal estimate of the joint axis. Methods for sample selection, that overcome the limitation of a dedicated initial calibration time window, are proposed. The sample selection allows estimation to be performed using only a small subset of samples from a larger data set as it deselects non-informative and redundant measurements. Finally, an uncertainty quantification method that assures validity of the estimated joint axis parameters, is proposed. Experimental validation of the method is provided using a mechanical joint performing a large range of motions. Angular errors in the order of 2 ∘ were achieved using 125–1000 selected samples. The proposed method is the first truly plug-and-play method that overcome the need for a specific calibration phase and, regardless of the user’s motions, it provides an accurate estimate of the joint axis as soon as possible.

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Section: Arbitrary-motionsmentioning

confidence: 99%

Robust Plug-and-Play Joint Axis Estimation Using Inertial Sensors

Olsson

Kok

Seel

et al. 2020

Sensors

Self Cite

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“…We now investigate whether this approach yields reliable RoM estimates in comparison to the more complex approach of attaching the second IMU directly onto the back of the hand. Moreover, the handmounted IMU serves as an approximate ground truth, since the accuracy of orientation and joint angle measurements from body-worn IMUs has been demonstrated to lie in the range of a few degrees in numerous studies [13,14,21,22].…”

Section: Range Of Motion Feasibility Evaluationmentioning

confidence: 99%

Development of a home-based wrist range-of-motion training system for children with cerebral palsy

Mittag

Leiss

Lorenz

et al. 2020

At - Automatisierungstechnik

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This paper presents the proof-of-concept of a home-based gamified wrist rehabilitation training system for children with cerebral palsy (CCP). We describe the user-centered design process of this system, which is composed of a wrist-worn inertial measurement unit (IMU) and a tangible device with an embedded IMU. The system employs a quaternion-based algorithm for automatic real-time estimation of the range of motion (RoM) covered by adduction/abduction and flexion/extension motions of the wrist. Experimental validation shows that the RoM can be determined with sufficient accuracy to control a game and that the algorithm is applicable in CCP. A serious game, which uses the presented algorithm and enables feedback as well as motivating stimuli, is implemented and evaluated by physiotherapists.

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“…Whenever quick motions of several body segments are tracked, the available bandwidth of the wireless network imposes limitations that lead to a trade-off between the number of sensors and the rate at which they can send their data in real time. In the worst case, this leads to cables being used despite many disadvantages [7].…”

Section: Introductionmentioning

confidence: 99%

Overcoming Bandwidth Limitations in Wireless Sensor Networks by Exploitation of Cyclic Signal Patterns: An Event-triggered Learning Approach

Beuchert

Solowjow

Trimpe

et al. 2020

Sensors

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Wireless sensor networks are used in a wide range of applications, many of which require real-time transmission of the measurements. Bandwidth limitations result in limitations on the sampling frequency and number of sensors. This problem can be addressed by reducing the communication load via data compression and event-based communication approaches. The present paper focuses on the class of applications in which the signals exhibit unknown and potentially time-varying cyclic patterns. We review recently proposed event-triggered learning (ETL) methods that identify and exploit these cyclic patterns, we show how these methods can be applied to the nonlinear multivariable dynamics of three-dimensional orientation data, and we propose a novel approach that uses Gaussian process models. In contrast to other approaches, all three ETL methods work in real time and assure a small upper bound on the reconstruction error. The proposed methods are compared to several conventional approaches in experimental data from human subjects walking with a wearable inertial sensor network. They are found to reduce the communication load by 60–70%, which implies that two to three times more sensor nodes could be used at the same bandwidth.

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A Tangible Solution for Hand Motion Tracking in Clinical Applications

Cited by 43 publications

References 50 publications

Robust Plug-and-Play Joint Axis Estimation Using Inertial Sensors

Robust Plug-and-Play Joint Axis Estimation Using Inertial Sensors

Development of a home-based wrist range-of-motion training system for children with cerebral palsy

Overcoming Bandwidth Limitations in Wireless Sensor Networks by Exploitation of Cyclic Signal Patterns: An Event-triggered Learning Approach

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