# Recognizing Activities of Daily Living with a Wrist-mounted Camera

arXiv: https://arxiv.org/abs/1511.06783 · HTML: https://mlsanity.com/paper/1511.06783/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2015.

## Built on by (most cited first, 6 of 6)

- [EgoSim: An Egocentric Multi-view Simulator and Real Dataset for Body-worn Cameras during Motion and Activity](https://mlsanity.com/paper/2502.18373/) (2025)
- [EgoRenderer: Rendering Human Avatars from Egocentric Camera Images](https://mlsanity.com/paper/2111.12685/) (2021)
- [Sensor-Augmented Egocentric-Video Captioning with Dynamic Modal Attention](https://mlsanity.com/paper/2109.02955/) (2021)
- [Proactive Conversational Assistant for a Procedural Manual Task based on Audio and IMU](https://mlsanity.com/paper/2602.15707/) (2026)
- [Egocentric Gaze Estimation via Neck-Mounted Camera](https://mlsanity.com/paper/2602.11669/) (2026)
- [Modular Retrieval-Augmented Generalization for Human Action Recognition](https://mlsanity.com/paper/2605.08117/) (2026)

## Abstract (arXiv)

We present a novel dataset and a novel algorithm for recognizing activities of daily living (ADL) from a first-person wearable camera. Handled objects are crucially important for egocentric ADL recognition. For specific examination of objects related to users' actions separately from other objects in an environment, many previous works have addressed the detection of handled objects in images captured from head-mounted and chest-mounted cameras. Nevertheless, detecting handled objects is not always easy because they tend to appear small in images. They can be occluded by a user's body. As described herein, we mount a camera on a user's wrist. A wrist-mounted camera can capture handled objects at a large scale, and thus it enables us to skip object detection process. To compare a wrist-mounted camera and a head-mounted camera, we also develop a novel and publicly available dataset that includes videos and annotations of daily activities captured simultaneously by both cameras. Additionally, we propose a discriminative video representation that retains spatial and temporal information after encoding frame descriptors extracted by Convolutional Neural Networks (CNN).