Human Activities Data set

Different Human Activities Gait Data set can be downloaded from here with permission of my on Dr. Vijay Bhaskar Semwal , vsemwal@gmail.com:

The data set is collected under joint project . The participating Institute parties of this data set collection are Dr. Vijay Bhaskar Semwal , MANIT Bhopal & Vishwanth Bijalwan, institute of technology Gopeshwar

As an agreement you have to cite following publications:

• Semwal, Vijay Bhaskar, and Gora Chand Nandi. "Toward developing a computational model for bipedal push recovery–a brief." IEEE Sensors Journal 15.4 (2015): 2021-2022.
• Semwal, Vijay Bhaskar, Neha Gaud, and G. C. Nandi. "Human gait state prediction using cellular automata and classification using ELM." Machine intelligence and signal analysis. Springer, Singapore, 2019. 135-145.
• Semwal, Vijay Bhaskar, and Gora Chand Nandi. "Generation of joint trajectories using hybrid automate-based model: a rocking block-based approach." IEEE Sensors Journal 16.14 (2016): 5805-5816.
• Semwal, Vijay Bhaskar, et al. "Design of vector field for different subphases of gait and regeneration of gait pattern." IEEE Transactions on Automation Science and Engineering 15.1 (2016): 104-110.
• Semwal, Vijay Bhaskar. "Data Driven Computational Model for Bipedal Walking and Push Recovery." arXiv preprint arXiv:1710.06548 (2017).
• Semwal, Vijay Bhaskar, Kaushik Mondal, and Gora Chand Nandi. "Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach." Neural Computing and Applications 28.3 (2017): 565-574.
• Patil, Prithvi, et al. "Clinical Human Gait Classification: Extreme Learning Machine Approach." 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT). IEEE, 2019.
• Semwal, Vijay Bhaskar, et al. "Biologically-inspired push recovery capable bipedal locomotion modeling through hybrid automata." Robotics and Autonomous Systems 70 (2015): 181-190.

Data Set of Different Activities

CSV Formatted Combined labeled data set

Data Set Description: The data set is collected for 25 subjects using wearable accelerometer (6-Degree IMU sensors) for 7 different activities named: Jogging, Normal walk, sit up, up stair walk, down stair walk, walk on heel and walk on toe for clinical examination purpose. We have considered the subject from different age group, gender and also collected data for pregnant woman data. Each data file is having following column: Time(s), ax(g), ay(g), az(g), wx(deg/s) , wy(deg/s), wz(deg/s), AngleX(deg) ,AngleY(deg) AngleZ(deg) T(°) .

Time(s) : time stamp

ax(g), ay(g), az(g): Acceleration about x,y and z axis

wx(deg/s) , wy(deg/s), wz(deg/s): Angular Velocity

AngleX(deg) ,AngleY(deg) AngleZ(deg) T(°): Joint angle value in degree.

The CNN code this model can be downloaded from here:

https://github.com/vsemwal/human-walking-activities/blob/master/har_with_cnn_initial_model.ipynb

For latest release of data set and new research you can follow this twitter page:

https://twitter.com/vsemwal/status/1252056564661248001

Results obtained after applying code. Download the code from GitHub link:

https://github.com/vsemwal/human-walking-activities

Other Relevant Github repositories :

https://github.com/lapalb/AI_Lab