Accuracy and Intrusiveness in Data-Driven Violin Players Skill Levels Prediction: MOCAP Against MYO Against KINECT

D’Amato, Vincenzo; Volta, Erica; Oneto, Luca; Volpe, Gualtiero; Camurri, Antonio; Anguita, Davide

doi:10.1007/978-3-030-85099-9_30

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…While a similarly specialist optical mocap technology was implemented in the Technology Enhanced Learning of Musical Instrument Performance (TELMI) project towards the development of technology-enhanced learning interfaces, authors Volpe et al [6] acknowledged a necessity for low-cost alternatives to their 12-camera array, for use by students and schools. Detailing Random Forest classification of participant skill level, D'Amato et al [3] reported an accuracy of 87.85% through the use of such a low-cost computer-vision alternative: the Kinekt. In comparison, the authors reported accuracies of 96.98% through use of a Qualysis mocap system, and 98.15% through use of the Myo Armband.…”

Section: A Gestural Sensorsmentioning

confidence: 99%

Ubiquitous Multimodality as a Tool in Violin Performance Classification

Wilson,

Granieri,

Ali-MacLachlan

2023

2023 4th International Symposium on the Internet of Sounds

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Through integrated sensors, wearable devices such as fitness trackers and smart-watches provide convenient interfaces by which multimodal time-series data may be recorded. Fostering multimodality in data collection allows for the observation of recorded actions, exercises or performances with consideration towards multiple transpiring aspects. This paper details an exploration of machine-learning based classification upon a dataset of audio-gestural violin recordings, collated through the use of a purpose-built smartwatch application. This interface allowed for the recording of synchronous gestural and audio data, which proved well-suited towards classification by deep neural networks (DNNs). Recordings were segmented into individual bow strokes, these were classified through completion of three tasks: Participant Recognition, Articulation Recognition, and Scale Recognition. Higher participant classification accuracies were observed through the use of lone gestural data, while multi-input deep neural networks (MI-DNNs) achieved varying increases in accuracy during completion of the latter two tasks, through concatenation of separate audio and gestural subnetworks. Across tasks and across network architectures, testclassification accuracies ranged between 63.83% and 99.67%. Articulation Recognition accuracies were consistently high, averaging 99.37%.

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Section: A Gestural Sensorsmentioning

confidence: 99%

Ubiquitous Multimodality as a Tool in Violin Performance Classification

Wilson,

Granieri,

Ali-MacLachlan

2023

2023 4th International Symposium on the Internet of Sounds

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“…From this director vector, the orientation changes are made using the gyroscope data. A unit quaternion from the angular velocity is calculated using Equation (1).…”

Section: Joint Anglesmentioning

confidence: 99%

“…Motion capture (MOCAP) is a technique that started around the 1970s using photogrammetry for biomechanical analysis. It is used in various fields such as education [1], sports training [2], and cinema and video games [3]. Its main purpose is to record the movements of an object, in this case the human body, for later analysis and to apply data modeling [4,5].…”

Section: Introductionmentioning

confidence: 99%

Vertical Jump Data from Inertial and Optical Motion Tracking Systems

Rico-García

Botero-Valencia

Hernández-García

2022

Data

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Motion capture (MOCAP) is a widely used technique to record human, animal, and object movement for various applications such as animation, biomechanical assessment, and control systems. Different systems have been proposed based on diverse technologies, such as visible light cameras, infrared cameras with passive or active markers, inertial systems, or goniometer-based systems. Each system has pros and cons that make it usable in different scenarios. This paper presents a dataset that combines Optical Motion and Inertial Systems, capturing a well-known sports movement as the vertical jump. As a reference system, the optical motion capture consists of six Flex 3 Optitrack cameras with 100 FPS. On the other hand, we developed an inertial system consisting of seven custom-made devices based on the IMU MPU-9250, which includes a three-axis magnetometer, accelerometer and gyroscope, and an embedded Digital Motion Processor (DMP) attached to a microcontroller mounted on a Teensy 3.2 with an ARM Cortex-M4 processor with wireless operation using Bluetooth. The purpose of taking IMU data with a low-cost and customized system is the deployment of applications that can be performed with similar hardware and can be adjusted to different areas. The developed measurement system is flexible, and the acquisition format and enclosure can be customized. The proposed dataset comprises eight jumps recorded from four healthy humans using both systems. Experimental results on the dataset show two usage examples for measuring joint angles and COM position. The proposed dataset is publicly available online and can be used in comparative algorithms, biomechanical studies, skeleton reconstruction, sensor fusion techniques, or machine learning models.

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