[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119664-en":3,"doc-seo-119664-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119664,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",6,"Technology","Smart Dance Shoes with Machine Learning Powered Light and Motion Synchronization - Project Overview","Wearable technology enables new forms of interaction and creativity in performance settings, and this project develops Smart Dance Shoes that synchronize RGB lighting in real time with a dancer’s movements using machine learning. The system uses an ESP32-S3 microcontroller with an MPU-6050 accelerometer/gyroscope to capture motion data, then classifies gestures such as jumps, spins, and steps through a model built on Edge Impulse. Classified outputs trigger corresponding lighting effects via RGB LED neon strips, supported by a lightweight portable battery power design and Bluetooth customization. Evaluation targets stability, low latency, and energy efficiency, with future work on multi-shoe synchronization, music-responsive lighting, and advanced models.","Smart Dance Shoes with Machine Learning Powered Light and Motion Synchronization  \nW.G.L. Harshani, E.M.R.S. Jayaweera, D.P.G.A.H. Kulathilaka, R.M.D.D. Malinda,  \nH.K. S. Theekshan and P. Ravindra S De Silva  \nDepartment of Computer Science, University ofSriJayewardenepura, Nugegoda, Sri Lanka Date Received: 23-09-2025 Date Accepted: 25-12-2025  \nAbstract  \nIn the era of wearable technology, integrating machine learning into performance arts opens new dimensions for user interaction and creativity. This project presents the development of Smart Dance Shoes that utilize motion sensors and machine learning algorithms to deliver real-time RGB light synchronization based on dance movements. The system is built using the ESP32-S3 microcontroller and the MPU-6050 sensor, which capture accelerometer and gyroscope data from the dancer’s movements. These data inputs are processed through a machine learning model developed on Edge Impulse, which classifies different dance gestures such as jumps, spins, and steps and triggers corresponding lighting effects to enhance visual performance. The hardware is designed to be lightweight, portable, and user-friendly, making it suitable for dancers, performers, and fitness enthusiasts. Key components include RGB LED neon strips, a 3.7V LiPo battery, and Bluetooth integration for wireless customization. Testing covered unit, integration, and performance evaluationsto ensure stability, low latency, and energy efficiency. Future improvements include multi-shoe synchronization, music-responsive lighting, and advanced models such as LSTM. This project demonstrates the potential of intelligent wearables in enhancing interactive and immersive experiences in performing arts.  \nKeywords: Wearable technology, Machine learning, Motion recognition, RGB lighting, Smart dance  \n1. Introduction  \nWearable technology has emerged as one of the fastest growing fields in HCI (Human Computer Interaction) by (Carroll, 2009), offering new ways to extend human capabilities and enhance everyday experiences (Pantelopoulos & Bourbakis, 2010),(Fortino et al., 2014) . From smartwatches that track health parameters to augmented reality headsets that redefine entertainment, wearables are now an integral part of modern life. In particular, motion responsive wearable devices have shown potential for creating immersive and engaging experiences in performance arts, sports, rehabilitation, and gaming (Liu et al., 2018),(Benbasat & Paradiso, 2003),(Gao et al., 2014) .  \nIn performance arts, interactivity and immersion are increasingly valued, with technology becoming a co-creative partner rather than just a background tool (Xu et al., 2016),(Dobrian & Bevilacqua, 2003) . Traditional performances often rely on lighting systems that are manually synchronized with music or choreography.  \nWhile visually effective, such setups lack flexibility, personalization, and real-time responsiveness. A dancer’s body movements, however, carry rich expressive information that can be captured using wearable sensors and mapped to dynamic lighting or sound effects. This coupling of motion recognition and actuation has been shown to enhance audience engagement and deepen the connection between performer and performance (Bevilacqua, Schnell & Rasamimanana, 2011),(Jensenius, 2007) . Recent advances in embedded machine learning, often referred to as “TinyML,” have enabled complex algorithms to run directly on resource-constrained devices such as microcontrollers (Warden & Situnayake, 2019) . These developments make it feasible to integrate real-time classification and decision-making within wearable platforms, eliminating the need for external computing resources. Devices like the ESP32-S3, combined with inertial measurement units (IMUs) such as the MPU-6050, provide a powerful yet low-cost solution for motion recognition tasks (Shah & Patel, 2019),(Gálvez et al., 2019). Meanwhile, RGB LED systems offer lightweight, portable, and programmable means of de","cbCaiv3l13amNMPl","https://ap.wps.com/l/cbCaiv3l13amNMPl","pdf",430952,1,13,"English","en",105,"# 1. Introduction\n## Wearable technology and motion-responsive interaction\n## Limitations of manual lighting synchronization\n## Proposed Smart Dance Shoes system and objectives\n## Hardware and machine learning approach (TinyML)","[{\"question\":\"How do the Smart Dance Shoes synchronize lighting with dance moves?\",\"answer\":\"The shoes capture acceleration and gyroscope data from the dancer’s motion using an MPU-6050 sensor connected to an ESP32-S3 microcontroller. A machine learning model classifies gestures in real time and triggers matching RGB lighting effects.\"},{\"question\":\"What sensors and compute platform are used in the prototype?\",\"answer\":\"The prototype integrates an ESP32-S3 microcontroller with an MPU-6050 inertial measurement unit (IMU) to collect motion signals. The machine learning model is deployed on the embedded device to avoid reliance on external computing.\"},{\"question\":\"What kinds of dance gestures can the system recognize?\",\"answer\":\"The described gesture set includes jumps, spins, and steps. The lighting patterns are triggered based on the classified gesture category.\"}]","Smart Dance Shoes with Machine Learning Powered Light and Motion Synchronization - Project Overview | PDF",1785725563,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"smart-dance-shoes-with-machine-learning-powered-light-and-motion-synchronization-project-overview","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/smart-dance-shoes-with-machine-learning-powered-light-and-motion-synchronization-project-overview/119664/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How do the Smart Dance Shoes synchronize lighting with dance moves?","Question",{"text":75,"@type":76},"The shoes capture acceleration and gyroscope data from the dancer’s motion using an MPU-6050 sensor connected to an ESP32-S3 microcontroller. A machine learning model classifies gestures in real time and triggers matching RGB lighting effects.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What sensors and compute platform are used in the prototype?",{"text":80,"@type":76},"The prototype integrates an ESP32-S3 microcontroller with an MPU-6050 inertial measurement unit (IMU) to collect motion signals. The machine learning model is deployed on the embedded device to avoid reliance on external computing.",{"name":82,"@type":73,"acceptedAnswer":83},"What kinds of dance gestures can the system recognize?",{"text":84,"@type":76},"The described gesture set includes jumps, spins, and steps. 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