A low-cost stand-alone platform for measuring motor behavior across developmental applications

Cavallo, Andrea; Foster, Nathan C.; Balasubramanian, Karthikeyan Kalyanasundaram; Merello, Andrea; Zini, Giorgio; Crepaldi, Marco; Becchio, Cristina

doi:10.1016/j.isci.2021.102742

Cited by 4 publications

(7 citation statements)

References 21 publications

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“…We aim to demonstrate multiple Bluetooth-based wearable device synchronization in a setup featuring Kinematics Detectors 1 (KiD, see Fig. 1 a) as hardware and using them across a developed User Interface (UI) as software.…”

Section: Resultsmentioning

confidence: 99%

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Neural network-based Bluetooth synchronization of multiple wearable devices

et al. 2023

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Bluetooth-enabled wearables can be linked to form synchronized networks to provide insightful and representative data that is exceptionally beneficial in healthcare applications. However, synchronization can be affected by inevitable variations in the component’s performance from their ideal behavior. Here, we report an application-level solution that embeds a Neural network to analyze and overcome these variations. The neural network examines the timing at each wearable node, recognizes time shifts, and fine-tunes a virtual clock to make them operate in unison and thus achieve synchronization. We demonstrate the integration of multiple Kinematics Detectors to provide synchronized motion capture at a high frequency (200 Hz) that could be used for performing spatial and temporal interpolation in movement assessments. The technique presented in this work is general and independent from the physical layer used, and it can be potentially applied to any wireless communication protocol.

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Section: Resultsmentioning

confidence: 99%

“…The adoption of Bluetooth-based wearable devices for the collection of physiological data in real-world scenarios 1 , 2 has seen a steady increase, primarily due to their portability, convenience, and safety attributes. Most applications, including those within the health sector 3 , require collection from multiple devices.…”

Section: Introductionmentioning

confidence: 99%

Neural network-based Bluetooth synchronization of multiple wearable devices

et al. 2023

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“…While low-cost biofeedback studies have been reported in the literature, their number is limited. Moreover, the existing literature reports that the hardware used was limited [ 1 ]; had a simple user interface for recording, event-marking, and downloading data without integration with the experiment control [ 91 ]; or required another computer to execute the experiment [ 26 ]. Some studies, such as event-related potential research, require precise biometric accuracy in milliseconds at the occurrence of the event of interest [ 92 ].…”

Section: Discussionmentioning

confidence: 99%

Building a Low-Cost Wireless Biofeedback Solution: Applying Design Science Research Methodology

Cheng

Lin

2023

Sensors

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In recent years, affective computing has emerged as a promising approach to studying user experience, replacing subjective methods that rely on participants’ self-evaluation. Affective computing uses biometrics to recognize people’s emotional states as they interact with a product. However, the cost of medical-grade biofeedback systems is prohibitive for researchers with limited budgets. An alternative solution is to use consumer-grade devices, which are more affordable. However, these devices require proprietary software to collect data, complicating data processing, synchronization, and integration. Additionally, researchers need multiple computers to control the biofeedback system, increasing equipment costs and complexity. To address these challenges, we developed a low-cost biofeedback platform using inexpensive hardware and open-source libraries. Our software can serve as a system development kit for future studies. We conducted a simple experiment with one participant to validate the platform’s effectiveness, using one baseline and two tasks that elicited distinct responses. Our low-cost biofeedback platform provides a reference architecture for researchers with limited budgets who wish to incorporate biometrics into their studies. This platform can be used to develop affective computing models in various domains, including ergonomics, human factors engineering, user experience, human behavioral studies, and human–robot interaction.

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“…A more promising direction for practical recording of movement data in real unconstrained settings may be hybrid tracking, for example, combining vision-based sensors (e.g. markerless motion capture) with wearable IMU sensors [19]. Another possibility, although further in the future, is the development of mobile motion capture equipment (perhaps drone-like) that does not interfere with the tracked activity or interaction, has capacity for constrained physical spaces such as in the home, and allows in-parallel zooming in and out of specific body regions of interest such as the ankle of a runner, the body-hands-head synchronization of a kayaker (for instance, the EuroMov flying carpet sensor system).…”

Section: Movement Sensorsmentioning

confidence: 99%

Human Movement Datasets: An Interdisciplinary Scoping Review

Olugbade

Bieńkiewicz²,

Barbareschi

et al. 2022

ACM Comput. Surv.

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Movement dataset reviews exist but are limited in coverage, both in terms of size and research discipline. While topic-specific reviews clearly have their merit, it is critical to have a comprehensive overview based on a systematic survey across disciplines. This enables higher visibility of datasets available to the research communities and can foster interdisciplinary collaborations. We present a catalogue of 704 open datasets described by 10 variables that can be valuable to researchers searching for secondary data: name and reference, creation purpose, data type, annotations, source, population groups, ordinal size of people captured simultaneously, URL, motion capture sensor, and funders. The catalogue is available in the supplementary materials. We provide an analysis of the datasets and further review them under the themes of human diversity, ecological validity, and data recorded. The resulting 12-dimension framework can guide researchers in planning the creation of open movement datasets. This work has been the interdisciplinary effort of researchers across affective computing, clinical psychology, disability innovation, ethnomusicology, human-computer interaction, machine learning, music cognition, music computing, and movement neuroscience.

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A low-cost stand-alone platform for measuring motor behavior across developmental applications

Cited by 4 publications

References 21 publications

Neural network-based Bluetooth synchronization of multiple wearable devices

Neural network-based Bluetooth synchronization of multiple wearable devices

Building a Low-Cost Wireless Biofeedback Solution: Applying Design Science Research Methodology

Human Movement Datasets: An Interdisciplinary Scoping Review

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