Software/Data

Badminton Activity Recognition (BAR)

The Badminton Activity Recognition (BAR) Dataset was collected for the sport of Badminton for 12 commonly played strokes. Besides the strokes, the objective of the dataset is to capture the associated leg movements. We believe in open access and thus data is made available without any password protection. If you use these datasets for your research, please cite the following dataset and paper. You can find the dataset in the following website link given below with the help of the DOI.

Access the dataset here.

Publication:

  1. Indrajeet Ghosh, Sreenivasan Ramasamy Ramamurthy, Nirmalya Roy, StanceScorer: A Data Driven Approach to Score Badminton Player, in Proceedings of the 6th IEEE International Workshop on Sensing Systems and Applications Using Wrist Worn Smart Devices (WristSense), co-located with PerCom, Austin, March 2020

Dataset Description:

  • Four Shimmer3 IMU Units were placed on the dominant palm and wrist, and both the legs at 512 Hz.

  • Each IMU unit comprises a 3-axis low-noise accelerometer, 3-axis high-noise accelerometer, 3-axis gyroscope, and 3-axis magnetometer.

  • The data was annotated for two aspects:

    • the strokes played (12 strokes),

    • how good the player executed the shot.

A Circuit level Green Building Dataset with Appliance, Room and Floor level Information for Energy Disaggregation.

We are making available a floor, room and an appliance level dataset for one of our locations. The data has been collected from a three-storied townhome (approx. 2000 sq. ft.) with a variety of appliances at the circuit level. Data is available at the minute-by-minute, hour-by-hour, and day-by-day level. We believe in open access and thus data is made available without any password protection. If you use these datasets for your research please cite the following papers. We plan to provide the subsequent circuit level datasets from this location in the due course of time. Stay tuned.

Publications:

  1. Nilavra Pathak, Md. Abdullah Al Hafiz Khan, and Nirmalya Roy. “Acoustic based appliance state identifications for fine grained energy analytics”, in Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom), March 2015.

  2. Nirmalya Roy, Nilavra Pathak, and Archan Misra. “AARPA: Combining Mobile and Power-line Sensing for Fine-grained Appliance Usage and Energy Monitoring”, in Proceedings of the IEEE International Conference on Mobile Data Management (MDM), June 2015.

Datasets:

Dataset Description:

  • Minute by minute data: wattage (power) by circuit, plus voltage for the whole house and outside temperature (in 7 days or 14 days chunk).

  • Hourly data: Kilowatt hours (energy usage) by circuit, plus average voltage for the whole house and outside temperature (in 30 days chunk).

  • Daily data: Daily kilowatt hours (energy usage) by circuit and outside temperature (in 6 months, 8 months or entire 1 year chunk).