mobile-sensing
There are 3 repositories under mobile-sensing topic.
mmalekzadeh / motion-sense
MotionSense Dataset for Human Activity and Attribute Recognition ( time-series data generated by smartphone's sensors: accelerometer and gyroscope) (PMC Journal) (IoTDI'19)
Language:Jupyter Notebook 312SensingKit / SensingKit-iOS
An iOS framework that provides Mobile Sensing functionality to your apps.
Language:Objective-C 106An Android framework that provides Mobile Sensing functionality to your apps.
Language:Java 89dapowan / LIMU-BERT-Public
A lite BERT-like representation model for IMU sensor data
Language:Python 74iantangc / ContrastiveLearningHAR
Contrastive Learning (SimCLR) for Human Activity Recognition
Language:Jupyter Notebook 65predictive-technology-laboratory / sensus
A Cross-Platform System for Mobile Sensing
Language:C# 37- Language:Python 29
cal-ucsd / ExtraSensoryAndroid
Mobile Android app for data collection in the wild: sensor measurements and self-reported labels describing the user's behavioral context.
Language:Java 25betamoo / Robots-Routing-using-Swarm-Intelligence
A C# project to simulate and test a multiagent algorithm for finding multiple noisy radiation sources with spatial and communication constraints with an emulated environment. The algorithm tries to detect the source(s) of radiation with some robots in the monitoring fields. Each robot has a sensor mounted to detect the radiation concentration. The robots cooperate and communicate with each other to locate the sources based on the sensors readings using concepts from particle swarm optimization algorithm. You can see the attached paper for more detail... [Multiagent Algorithm for finding Multiple Noisy Radiation.pdf](Home_Multiagent Algorithm for finding Multiple Noisy Radiation.pdf)
Language:C# 4- Language:SCSS 2
- Language:Java 2
- mpathsenserLanguage:R 1
KSwaviman / Cracking-The-Personality-Code-A-Behavioral-Research
Our study focused on using the Big Five personality inventory to predict traits from students' smartphone sensor data collected over 2 months under the Horizon Europe project. Through correlation analyses and machine learning with cross-validation, we showed that predictions are reliable and accurate enough for practical use.
Language:TeX 1ceat-epfl / sensecity-africa
Crowdsensing urban data in Africa
Language:Jupyter Notebook 0sustainable-computing / Blinder
Code for "Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated Learning", ACM ToSN
Language:Python 0sogoagain / android-mobile-system-programming
모바일 시스템 프로그래밍 (센싱)
Language:Java
