ForcePose: A Deep Learning Approach for Force Calculation Based on Action Recognition Using MediaPipe Pose Estimation Combined with Object Detection
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916664887476224 |
|---|---|
| author | M, Nandakishor Govind V, Vrinda Puthalath, Anuradha L, Anzy S, Swathi P R, Aswathi R, Devaprabha A Raj, Varsha K, Midhuna Krishnan T V, Akhila Anilkumar P V, Yamuna |
| author_facet | M, Nandakishor Govind V, Vrinda Puthalath, Anuradha L, Anzy S, Swathi P R, Aswathi R, Devaprabha A Raj, Varsha K, Midhuna Krishnan T V, Akhila Anilkumar P V, Yamuna |
| contents | Force estimation in human-object interactions is crucial for various fields like ergonomics, physical therapy, and sports science. Traditional methods depend on specialized equipment such as force plates and sensors, which makes accurate assessments both expensive and restricted to laboratory settings. In this paper, we introduce ForcePose, a novel deep learning framework that estimates applied forces by combining human pose estimation with object detection. Our approach leverages MediaPipe for skeletal tracking and SSD MobileNet for object recognition to create a unified representation of human-object interaction. We've developed a specialized neural network that processes both spatial and temporal features to predict force magnitude and direction without needing any physical sensors. After training on our dataset of 850 annotated videos with corresponding force measurements, our model achieves a mean absolute error of 5.83 N in force magnitude and 7.4 degrees in force direction. When compared to existing computer vision approaches, our method performs 27.5% better while still offering real-time performance on standard computing hardware. ForcePose opens up new possibilities for force analysis in diverse real-world scenarios where traditional measurement tools are impractical or intrusive. This paper discusses our methodology, the dataset creation process, evaluation metrics, and potential applications across rehabilitation, ergonomics assessment, and athletic performance analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22363 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ForcePose: A Deep Learning Approach for Force Calculation Based on Action Recognition Using MediaPipe Pose Estimation Combined with Object Detection M, Nandakishor Govind V, Vrinda Puthalath, Anuradha L, Anzy S, Swathi P R, Aswathi R, Devaprabha A Raj, Varsha K, Midhuna Krishnan T V, Akhila Anilkumar P V, Yamuna Computer Vision and Pattern Recognition Artificial Intelligence Force estimation in human-object interactions is crucial for various fields like ergonomics, physical therapy, and sports science. Traditional methods depend on specialized equipment such as force plates and sensors, which makes accurate assessments both expensive and restricted to laboratory settings. In this paper, we introduce ForcePose, a novel deep learning framework that estimates applied forces by combining human pose estimation with object detection. Our approach leverages MediaPipe for skeletal tracking and SSD MobileNet for object recognition to create a unified representation of human-object interaction. We've developed a specialized neural network that processes both spatial and temporal features to predict force magnitude and direction without needing any physical sensors. After training on our dataset of 850 annotated videos with corresponding force measurements, our model achieves a mean absolute error of 5.83 N in force magnitude and 7.4 degrees in force direction. When compared to existing computer vision approaches, our method performs 27.5% better while still offering real-time performance on standard computing hardware. ForcePose opens up new possibilities for force analysis in diverse real-world scenarios where traditional measurement tools are impractical or intrusive. This paper discusses our methodology, the dataset creation process, evaluation metrics, and potential applications across rehabilitation, ergonomics assessment, and athletic performance analysis. |
| title | ForcePose: A Deep Learning Approach for Force Calculation Based on Action Recognition Using MediaPipe Pose Estimation Combined with Object Detection |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2503.22363 |