# Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition using Wrist-Worn Inertial Sensors

arXiv: https://arxiv.org/abs/2305.13124 · HTML: https://mlsanity.com/paper/2305.13124/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 115 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2001.07416/) (2020)
- [Improving Deep Learning for HAR with shallow LSTMs](https://mlsanity.com/paper/2108.00702/) (2021)
- [Detecting events and key actors in multi-person videos](https://mlsanity.com/paper/1511.02917/) (2015)
- [Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey](https://mlsanity.com/paper/2004.08821/) (2020)
- [Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors](https://mlsanity.com/paper/2007.07172/) (2020)
- [What Players do with the Ball: A Physically Constrained Interaction Modeling](https://mlsanity.com/paper/1511.06181/) (2015)
- [Group Activity Recognition in Basketball Tracking Data -- Neural Embeddings in Team Sports (NETS)](https://mlsanity.com/paper/2209.00451/) (2022)

## Built on by (most cited first, 11 of 11)

- [SETransformer: A Hybrid Attention-Based Architecture for Robust Human Activity Recognition](https://mlsanity.com/paper/2505.19369/) (2025)
- [XRF V2: A Dataset for Action Summarization with Wi-Fi Signals, and IMUs in Phones, Watches, Earbuds, and Glasses](https://mlsanity.com/paper/2501.19034/) (2025)
- [Temporal Action Localization for Inertial-based Human Activity Recognition](https://mlsanity.com/paper/2311.15831/) (2023)
- [Weak-Annotation of HAR Datasets using Vision Foundation Models](https://mlsanity.com/paper/2408.05169/) (2024)
- [Evaluating Spiking Neural Network On Neuromorphic Platform For Human Activity Recognition](https://mlsanity.com/paper/2308.00787/) (2023)
- [SlotFM: A Motion Foundation Model with Slot Attention for Diverse Downstream Tasks](https://mlsanity.com/paper/2509.21673/) (2025)
- [γ-Quant: Towards Learnable Quantization for Low-bit Pattern Recognition](https://mlsanity.com/paper/2509.22448/) (2025)
- [HARMES: A Multi-Modal Dataset for Wearable Human Activity Recognition with Motion, Environmental Sensing and Sound](https://mlsanity.com/paper/2605.02596/) (2026)
- [DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity Recognition](https://mlsanity.com/paper/2505.20894/) (2025)
- [WS-IMUBench: Can Weakly Supervised Methods from Audio, Image, and Video Be Adapted for IMU-based Temporal Action Localization?](https://mlsanity.com/paper/2602.01850/) (2026)
- [WHAR Arena: Benchmarking the State of the Art in Efficient Wearable Human Activity Recognition](https://mlsanity.com/paper/2606.13194/) (2026)

## Abstract (arXiv)

We present a benchmark dataset for evaluating physical human activity recognition methods from wrist-worn sensors, for the specific setting of basketball training, drills, and games. Basketball activities lend themselves well for measurement by wrist-worn inertial sensors, and systems that are able to detect such sport-relevant activities could be used in applications toward game analysis, guided training, and personal physical activity tracking. The dataset was recorded for two teams from separate countries (USA and Germany) with a total of 24 players who wore an inertial sensor on their wrist, during both repetitive basketball training sessions and full games. Particular features of this dataset include an inherent variance through cultural differences in game rules and styles as the data was recorded in two countries, as well as different sport skill levels, since the participants were heterogeneous in terms of prior basketball experience. We illustrate the dataset's features in several time-series analyses and report on a baseline classification performance study with two state-of-the-art deep learning architectures.