# An Effective and Efficient Method for Detecting Hands in Egocentric Videos for Rehabilitation Applications

arXiv: https://arxiv.org/abs/1908.10406 · HTML: https://mlsanity.com/paper/1908.10406/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2019.

## Built on by (most cited first, 7 of 7)

- [Visual Object Tracking in First Person Vision](https://mlsanity.com/paper/2209.13502/) (2022)
- [Is First Person Vision Challenging for Object Tracking?](https://mlsanity.com/paper/2108.13665/) (2021)
- [Measuring hand use in the home after cervical spinal cord injury using egocentric video](https://mlsanity.com/paper/2203.16996/) (2022)
- [Benchmarking 2D Egocentric Hand Pose Datasets](https://mlsanity.com/paper/2409.07337/) (2024)
- [Visuo-Acoustic Hand Pose and Contact Estimation](https://mlsanity.com/paper/2508.00852/) (2025)
- [A Personalized Video-Based Hand Taxonomy: Application for Individuals with Spinal Cord Injury](https://mlsanity.com/paper/2403.18094/) (2024)
- [REST-HANDS: Rehabilitation with Egocentric Vision Using Smartglasses for Treatment of Hands after Surviving Stroke](https://mlsanity.com/paper/2409.20116/) (2024)

## Abstract (arXiv)

Objective: Individuals with spinal cord injury (SCI) report upper limb function as their top recovery priority. To accurately represent the true impact of new interventions on patient function and independence, evaluation should occur in a natural setting. Wearable cameras can be used to monitor hand function at home, using computer vision to automatically analyze the resulting videos (egocentric video). A key step in this process, hand detection, is difficult to do robustly and reliably, hindering deployment of a complete monitoring system in the home and community. We propose an accurate and efficient hand detection method that uses a simple combination of existing detection and tracking algorithms. Methods: Detection, tracking, and combination methods were evaluated on a new hand detection dataset, consisting of 167,622 frames of egocentric videos collected on 17 individuals with SCI performing activities of daily living in a home simulation laboratory. Results: The F1-scores for the best detector and tracker alone (SSD and Median Flow) were 0.90±0.07 and 0.42±0.18, respectively. The best combination method, in which a detector was used to initialize and reset a tracker, resulted in an F1-score of 0.87±0.07 while being two times faster than the fastest detector alone. Conclusion: The combination of the fastest detector and best tracker improved the accuracy over online trackers while improving the speed of detectors. Significance: The method proposed here, in combination with wearable cameras, will help clinicians directly measure hand function in a patient's daily life at home, enabling independence after SCI.