Real-time Multi-modal Comprehensive Bodyweight Exercise Feedback System with a Personal Virtual Expert Guiding

Bodyweight exercises have numerous health benefits when performed correctly. Yet, such exercises require the supervision of a professional to prevent injuries while maximizing the health benefits. However, gaining access to an expert can be challenging due to financial burdens or scheduling concerns. In this paper, we present a real-time multi-modal exercise feedback system based on experts' data, aiming to provide bodyweight exercise guidance to users. In this context, we utilized body pose and floor-based pressure data for bodyweight squats, lunges, and crunches. After recording the exercise data of certified fitness trainers, we created our virtual experts to provide expert-aligned motion and force feedback to the users on our platform. Users were given the option to select virtual experts depending on their fitness goals, while the expert data was personalized for real-time feedback. As a result, we observed significant improvements in users' movement and force during the exercise sessions, along with greater preference and heightened motion awareness.

Authors

Institutions

Publication Details

Journal
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
Published
2026-09-30
DOI
https://doi.org/10.1145/3831650
Primary Topic
Human Motion and Animation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Real-time Multi-modal Comprehensive Bodyweight Exercise Feedback System with a Personal Virtual Expert Guiding

Yiyue Luo, Wojciech Matusik, Joseph DelPreto, Ecehan Akan et al.
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
Human Motion and Animation
article

Real-time Multi-modal Comprehensive Bodyweight Exercise Feedback System with a Personal Virtual Expert Guiding

Yiyue Luo, Wojciech Matusik, Joseph DelPreto, Ecehan Akan, Hosu Lee, Eunhee Kim, Daniela L. Rus, Kyung-Joong Kim, Yun‐Ho Choi, Jin-Ha Noh
article en

Abstract

Bodyweight exercises have numerous health benefits when performed correctly. Yet, such exercises require the supervision of a professional to prevent injuries while maximizing the health benefits. However, gaining access to an expert can be challenging due to financial burdens or scheduling concerns. In this paper, we present a real-time multi-modal exercise feedback system based on experts' data, aiming to provide bodyweight exercise guidance to users. In this context, we utilized body pose and floor-based pressure data for bodyweight squats, lunges, and crunches. After recording the exercise data of certified fitness trainers, we created our virtual experts to provide expert-aligned motion and force feedback to the users on our platform. Users were given the option to select virtual experts depending on their fitness goals, while the expert data was personalized for real-time feedback. As a result, we observed significant improvements in users' movement and force during the exercise sessions, along with greater preference and heightened motion awareness.

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous TechnologiesVol. 10(3)
Gyeongsang National University (KR), University of Washington (US), Gwangju Institute of Science and Technology (KR), Massachusetts Institute of Technology (US)
Good health and well-being
Openalex Percentile: Top 16%
Human Motion and Animation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Real-time Multi-modal Comprehensive Bodyweight Exercise Feedback System with a Personal Virtual Expert Guiding — Yiyue Luo, Wojciech Matusik, et al. · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (2026) | TGRS Research Map | TGRS