# UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones

arXiv: https://arxiv.org/abs/1611.07688 · HTML: https://mlsanity.com/paper/1611.07688/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2016.

## Built on by (most cited first, 30 of 54)

- [Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey](https://mlsanity.com/paper/2302.02515/) (2023)
- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
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- [Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition](https://mlsanity.com/paper/2206.06629/) (2022)
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- [Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition](https://mlsanity.com/paper/2310.18562/) (2023)
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- [HARMamba: Efficient and Lightweight Wearable Sensor Human Activity Recognition Based on Bidirectional Mamba](https://mlsanity.com/paper/2403.20183/) (2024)
- [Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects](https://mlsanity.com/paper/2202.03274/) (2022)
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- [BioDiffusion: A Versatile Diffusion Model for Biomedical Signal Synthesis](https://mlsanity.com/paper/2401.10282/) (2024)
- [Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review](https://mlsanity.com/paper/2403.15422/) (2024)
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- [Dynamic Black-box Backdoor Attacks on IoT Sensory Data](https://mlsanity.com/paper/2511.14074/) (2025)
- [Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V Processors](https://mlsanity.com/paper/2205.12781/) (2022)
- [A Survey of Few-Shot Learning for Biomedical Time Series](https://mlsanity.com/paper/2405.02485/) (2024)
- [Time Series Embedding Methods for Classification Tasks: A Review](https://mlsanity.com/paper/2501.13392/) (2025)
- [Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks](https://mlsanity.com/paper/2209.00839/) (2022)
- [Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural Networks](https://mlsanity.com/paper/2212.02233/) (2022)
- [A Survey of Transformer Enabled Time Series Synthesis](https://mlsanity.com/paper/2406.02322/) (2024)
- [IMUDiffusion: A Diffusion Model for Multivariate Time Series Synthetisation for Inertial Motion Capturing Systems](https://mlsanity.com/paper/2411.02954/) (2024)
- [Transformer-based conditional generative adversarial network for multivariate time series generation](https://mlsanity.com/paper/2210.02089/) (2022)
- [A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation](https://mlsanity.com/paper/2303.11100/) (2023)
- [P2LHAP:Wearable sensor-based human activity recognition, segmentation and forecast through Patch-to-Label Seq2Seq Transformer](https://mlsanity.com/paper/2403.08214/) (2024)
- [Adaptive Random Forests for Energy-Efficient Inference on Microcontrollers](https://mlsanity.com/paper/2205.13838/) (2022)
- [BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition](https://mlsanity.com/paper/2208.03647/) (2022)
- [Dynamic Decision Tree Ensembles for Energy-Efficient Inference on IoT Edge Nodes](https://mlsanity.com/paper/2306.09789/) (2023)

## Abstract (arXiv)

Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are being increasingly used to monitor human activities. Data acquired by the hosted sensors are usually processed by machine-learning-based algorithms to classify human activities. The success of those algorithms mostly depends on the availability of training (labeled) data that, if made publicly available, would allow researchers to make objective comparisons between techniques. Nowadays, publicly available data sets are few, often contain samples from subjects with too similar characteristics, and very often lack of specific information so that is not possible to select subsets of samples according to specific criteria. In this article, we present a new dataset of acceleration samples acquired with an Android smartphone designed for human activity recognition and fall detection. The dataset includes 11,771 samples of both human activities and falls performed by 30 subjects of ages ranging from 18 to 60 years. Samples are divided in 17 fine grained classes grouped in two coarse grained classes: one containing samples of 9 types of activities of daily living (ADL) and the other containing samples of 8 types of falls. The dataset has been stored to include all the information useful to select samples according to different criteria, such as the type of ADL, the age, the gender, and so on. Finally, the dataset has been benchmarked with four different classifiers and with two different feature vectors. We evaluated four different classification tasks: fall vs no fall, 9 activities, 8 falls, 17 activities and falls. For each classification task we performed a subject-dependent and independent evaluation. The major findings of the evaluation are the following: i) it is more difficult to distinguish between types of falls than types of activities; ii) subject-dependent evaluation outperforms the subject-independent one