# Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning

arXiv: https://arxiv.org/abs/2201.08565 · HTML: https://mlsanity.com/paper/2201.08565/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 21 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [Recognizing Detailed Human Context In-the-Wild from Smartphones and Smartwatches](https://mlsanity.com/paper/1609.06354/) (2016)
- [Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection](https://mlsanity.com/paper/2109.05701/) (2021)
- [Machine Learning Algorithms In User Authentication Schemes](https://mlsanity.com/paper/2110.07826/) (2021)
- [Online Human Activity Recognition using Low-Power Wearable Devices](https://mlsanity.com/paper/1808.08615/) (2018)
- [A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods](https://mlsanity.com/paper/2110.07832/) (2021)
- [Continuous Authentication Using Mouse Movements, Machine Learning, and Minecraft](https://mlsanity.com/paper/2110.11080/) (2021)

## Built on by (most cited first, 8 of 8)

- [Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning](https://mlsanity.com/paper/2205.08371/) (2022)
- [Leveraging Deep Learning Approaches for Deepfake Detection: A Review](https://mlsanity.com/paper/2304.01908/) (2023)
- [Hybrid Deepfake Detection Utilizing MLP and LSTM](https://mlsanity.com/paper/2304.14504/) (2023)
- [Your Identity is Your Behavior -- Continuous User Authentication based on Machine Learning and Touch Dynamics](https://mlsanity.com/paper/2305.09482/) (2023)
- [Your device may know you better than you know yourself -- continuous authentication on novel dataset using machine learning](https://mlsanity.com/paper/2403.03832/) (2024)
- [Mitigating Presentation Attack using DCGAN and Deep CNN](https://mlsanity.com/paper/2207.00161/) (2022)
- [Machine Learning Based Approach to Recommend MITRE ATT&CK Framework for Software Requirements and Design Specifications](https://mlsanity.com/paper/2302.05530/) (2023)
- [Recent Advancements In The Field Of Deepfake Detection](https://mlsanity.com/paper/2308.05563/) (2023)

## Abstract (arXiv)

Human activity recognition has grown in popularity with its increase of applications within daily lifestyles and medical environments. The goal of having efficient and reliable human activity recognition brings benefits such as accessible use and better allocation of resources; especially in the medical industry. Activity recognition and classification can be obtained using many sophisticated data recording setups, but there is also a need in observing how performance varies among models that are strictly limited to using sensor data from easily accessible devices: smartphones and smartwatches. This paper presents the findings of different models that are limited to train using such sensors. The models are trained using either the k-Nearest Neighbor, Support Vector Machine, or Random Forest classifier algorithms. Performance and evaluations are done by comparing various model performances using different combinations of mobile sensors and how they affect recognitive performances of models. Results show promise for models trained strictly using limited sensor data collected from only smartphones and smartwatches coupled with traditional machine learning concepts and algorithms.