mobile-sensing
There are 20 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)
SensingKit/SensingKit-iOS
An iOS framework that provides Mobile Sensing to your apps.
SensingKit/SensingKit-Android
An Android framework that provides Mobile Sensing to your apps.
dapowan/LIMU-BERT-Public
A lite BERT-like representation model for IMU sensor data
iantangc/ContrastiveLearningHAR
Contrastive Learning (SimCLR) for Human Activity Recognition
predictive-technology-laboratory/sensus
A Cross-Platform System for Mobile Sensing
iantangc/SelfHAR
Improving Human Activity Recognition through Self-training with Unlabeled Data
cal-ucsd/ExtraSensoryAndroid
Mobile Android app for data collection in the wild: sensor measurements and self-reported labels describing the user's behavioral context.
stevenshci/PupilSense
Official implementation of the pupillometry system called PupilSense proposed in the article "PupilSense: Detection of Depressive Episodes Through Pupillary Response in the Wild".
rh20624/Awesome-IMU-Sensing
A collection of datasets, papers, and resources for IMU sensing.
betamoo/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)
koenniem/mpathsenser
Mirror of the mpathsenser package from Gitlab.
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.
ceat-epfl/sensecity-africa
Crowdsensing urban data in Africa
sustainable-computing/Blinder
Code for "Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated Learning", ACM ToSN
sogoagain/android-mobile-system-programming
모바일 시스템 프로그래밍 (센싱)