HappyFeet

Abstract

In this paper, we discuss our experience in building an automated dance assessment tool with IMU and IoT devices and highlight the major challenges of such an endeavor. In a typical dance classroom scenario, where the students frequently outnumber their instructors, such a system can add an immense value to both parties by providing systematic breakdown of the dance moves, comparing the dance moves between the students and the instructors, and pinpointing the places for improvement in an autonomous way. Along that direction, our prototypical work, HappyFeet [1], showcases our initial attempts of developing such an intelligent Dance Activity Recognition (DAR) system. Our CNN based Body Sensor Network proves more effective (by ≈7% margin at 94.20%) at accurately recognizing the micro-steps of the dance activities than traditional feature engineering approaches. These metrics are derived by purposely evaluating the setup on a dance form known for its gentle, smooth and subtle limb movements. In this paper, we articulate how our proposed DAR framework will be generalizable for diverse dance styles involving very pronounced movements, human body kinematics and energy profiles.

Zero-Interaction Security-Towards Sound Experimental Validation

“…Similar to prior research [6], we found sensor bias among heterogeneous devices, most notably in barometric pressure readings. We also observed sampling rate instability among smartphones of the same model, especially for IMU sensors (tested with Galaxy S6, Nexus 5X, and 6P), with the actual sampling rate deviating by up to 10% from the set value.…”

[Retracted] Dance Motion Capture Based on Data Fusion Algorithm and Wearable Sensor Network

“…Motion capture is a technology that uses corresponding sensors to sample the motion data of a target object, record its motion process, and then process the data through a computer to finally achieve functions such as restoration display, classification modelling, or analysis research of the object's motion [2]. In some cases, motion capture treats the target moving object as a whole and records only its movement trajectory in three-dimensional space, so it is also called motion tracking, while in some special application scenarios, it may need to record extremely complex motion information, such as human facial expressions, swelling changes of objects, and it is usually called performance capture when such tiny and precise expressions need to be captured [3].…”

Research on the identification and integration of folk dance creation elements based on big data technology

“…For a long time, these folk dances have been inherited from generation to generation in a spontaneous state, without any technical media intervention between the transmitter and the recipient, only oral and face-to-face direct transmission, which is an individual and single mode of transmission, and the development and participation of many dances have been less optimistic [2][3]. As people's aesthetic requirements increase and the level of artistry deepens, original folk dances gradually require to be standardized and specialized, and they need to be constantly innovated in the process of artistic creation, which requires the intervention of big data and digital technology to obtain and preserve key information about the dance [4][5]. For folk dances themselves, the same folk dance usually has some iconic movements, gestures, and props to express, especially for some dances with strong ethnic characteristics [6][7].…”