Compact, Accurate and Low-cost Hand Tracking System based on LEAP Motion Controllers and Raspberry Pi

Placidi, Giuseppe; Matteo, Alessandro Di; Mignosi, Filippo; Polsinelli, Matteo; Spezialetti, Matteo

doi:10.5220/0010880900003122

Cited by 5 publications

(7 citation statements)

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“…Hand tracking is ensured by VG [ 33 ], a multisensor touchless device. The advantages of touchless systems are that they do not interfere with the hand’s free movement and are user-independent, meaning that they do not demand personalized equipment, or in the other case, equipment that many people should touch [ 16 ].…”

Section: The Cooperative Telerehabilitation Frameworkmentioning

confidence: 99%

“…For this reason, the original architecture of the VG used a primary-secondary system configuration where each sensor was controlled by one of two virtual machines (Secondaries) hosted on a physical machine (Primary), where the data fusion also took place. In a second embodiment, taking advantage of the progress in the field of microprocessors, the PC hosting three servers was replaced by three Raspberry Pie (RP) machines [ 33 ], providing us with an economical and space-efficient solution. In this scheme, the raspberry Pie operating system is installed in every RP, and each LMC is associated with one machine.…”

Section: The Cooperative Telerehabilitation Frameworkmentioning

confidence: 99%

“…In this paper, we present a novel framework for hand telerehabilitation that allows cooperation between patients and therapists and includes a CV-based system named Virtual Glove (VG) [ 29 , 30 , 31 , 32 , 33 ] for real-time hand tracking (40 fps), which is transduced in a light, cheap, simple, and precise system to be used at home. The proposed architecture is designed to: support both supervised and unsupervised therapy; allow remote or “in-presence” therapy; support a client mode usage, robust in the case of a weak Internet connection; permit saving the history of the rehabilitation process of a person; provide an interface to the therapist for monitoring the rehabilitation progresses, managing patient and assigned rehabilitation tasks, data processing, and cooperating with other therapists by supporting data-sharing; recognize an administrator user that can manage the whole system.…”

Section: Introductionmentioning

confidence: 99%

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Patient–Therapist Cooperative Hand Telerehabilitation through a Novel Framework Involving the Virtual Glove System

Placidi

Matteo

Lozzi

et al. 2023

Sensors

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Telerehabilitation is important for post-stroke or post-surgery rehabilitation because the tasks it uses are reproducible. When combined with assistive technologies, such as robots, virtual reality, tracking systems, or a combination of them, it can also allow the recording of a patient’s progression and rehabilitation monitoring, along with an objective evaluation. In this paper, we present the structure, from actors and functionalities to software and hardware views, of a novel framework that allows cooperation between patients and therapists. The system uses a computer-vision-based system named virtual glove for real-time hand tracking (40 fps), which is translated into a light and precise system. The novelty of this work lies in the fact that it gives the therapist quantitative, not only qualitative, information about the hand’s mobility, for every hand joint separately, while at the same time providing control of the result of the rehabilitation by also quantitatively monitoring the progress of the hand mobility. Finally, it also offers a strategy for patient–therapist interaction and therapist–therapist data sharing.

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Section: The Cooperative Telerehabilitation Frameworkmentioning

confidence: 99%

Section: The Cooperative Telerehabilitation Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Patient–Therapist Cooperative Hand Telerehabilitation through a Novel Framework Involving the Virtual Glove System

Placidi

Matteo

Lozzi

et al. 2023

Sensors

Self Cite

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“…The LMC-based solution of the VG serves to enhance the precision and efficiency of hand-tracking in the rehabilitation process. The VG is a component of a complete framework for hand telerehabilitation [46,47] that also involves active with therapists. Within this framework, the therapist can assign specific rehabilitation tasks to the patient, set execution parameters, and conduct quantitative analyses from the resulting collected data.…”

Section: Non-conservative Solutionmentioning

confidence: 99%

A DICOM-based Standard for Quantitative Physical Rehabilitation

Matteo,

Lozzi,

Mignosi

et al. 2023

Preprint

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Physical rehabilitation (PR) is crucial in medicine that, yet to date, often relies on qualitative rather than objectively quantifiable data. With the recent scientific and technological advances, several instruments and methods have been proposed aimed at measuring and evaluating rehabilitation objectively, for quantitative PR (QPR), though without a standardized data format. The lack of a standard data format implies several issues, including the inability to compare datafrom different devices, create patient history, perform temporal evaluation or inter-patient comparisons, share data, create and use common evaluation scalesfor therapists, calculate statistics, etc. This lack is also an obstacle to the diffusion of artificial intelligence in rehabilitation, due to the data inhomogeneity,increasing uncertainty, and subjectivity. Digital Imaging and COmmunicationsin Medicine (DICOM) is an affirmed standard, born for medical imaging but recently extended to other fields of medicine, with the primary scope to share data from different devices, handle different associated processes, and assure interoperability among systems and specialists, by generating structured data and documents. In this article, a DICOM Information Object Definition (IOD) is proposed for QPR data. Furthermore, as an example, the proposed DICOM-like standard is applied for managing data from QPR of the hand, generated by the Virtual Glove (VG) device, a multi-source hand-tracking device, and results are discussed.

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“…With the recent advancements in single-board computers (SBC), powerful, compact, and portable computing devices have become available. These devices are capable of managing LMC sensors at sufficient frame rates [15].…”

Section: Introductionmentioning

confidence: 99%

Portable Head-Mounted System for Mobile Forearm Tracking

Polsinelli,

Di Matteo,

Lozzi

et al. 2024

Sensors

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Computer vision (CV)-based systems using cameras and recognition algorithms offer touchless, cost-effective, precise, and versatile hand tracking. These systems allow unrestricted, fluid, and natural movements without the constraints of wearable devices, gaining popularity in human–system interaction, virtual reality, and medical procedures. However, traditional CV-based systems, relying on stationary cameras, are not compatible with mobile applications and demand substantial computing power. To address these limitations, we propose a portable hand-tracking system utilizing the Leap Motion Controller 2 (LMC) mounted on the head and controlled by a single-board computer (SBC) powered by a compact power bank. The proposed system enhances portability, enabling users to interact freely with their surroundings. We present the system’s design and conduct experimental tests to evaluate its robustness under variable lighting conditions, power consumption, CPU usage, temperature, and frame rate. This portable hand-tracking solution, which has minimal weight and runs independently of external power, proves suitable for mobile applications in daily life.

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Compact, Accurate and Low-cost Hand Tracking System based on LEAP Motion Controllers and Raspberry Pi

Cited by 5 publications

References 0 publications

Patient–Therapist Cooperative Hand Telerehabilitation through a Novel Framework Involving the Virtual Glove System

Patient–Therapist Cooperative Hand Telerehabilitation through a Novel Framework Involving the Virtual Glove System

A DICOM-based Standard for Quantitative Physical Rehabilitation

Portable Head-Mounted System for Mobile Forearm Tracking

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