Thu 10.14.21
Thu 10.14.21
Thu 10.14.21
Thu 10.14.21
Human activity recognition using wearable accelerometers can enable in-situ detection of physical activities to support novel human-computer interfaces. Activity recognition algorithms use data on the motion and orientation of limbs to detect activities such as walking, sitting, and sleeping, among others. Many of the machine-learning-based algorithms require multi-person, multi-day, carefully-annotated training data with precisely marked start and end times of the activities of interest. To date, there is a dearth of usable tools that enable researchers to conveniently visualize and annotate multiple days of high-sampling-rate raw accelerometer data.
We developed “Signaligner Pro” – a data annotation tool to enable researchers to conveniently and quickly explore and annotate multi-day high-sampling rate raw sensor data with the assistance from state-of-the-art activity recognition algorithms. The tool visualizes high-sampling rate raw data and time-stamped annotations generated by existing activity recognition algorithms; the annotations can then be directly modified by the researchers to create their own, improved, labeled datasets.
Signaligner Pro is an open source tool available here to download. Please get in touch if you want to contribute to the code base here. Access to repo is available on request.
For more information, visit the detailed project page.
(Credit: Aditya Ponnada)
Human activity recognition using wearable accelerometers can enable in-situ detection of physical activities to support novel human-computer interfaces. Activity recognition algorithms use data on the motion and orientation of limbs to detect activities such as walking, sitting, and sleeping, among others. Many of the machine-learning-based algorithms require multi-person, multi-day, carefully-annotated training data with precisely marked start and end times of the activities of interest. To date, there is a dearth of usable tools that enable researchers to conveniently visualize and annotate multiple days of high-sampling-rate raw accelerometer data.
We developed “Signaligner Pro” – a data annotation tool to enable researchers to conveniently and quickly explore and annotate multi-day high-sampling rate raw sensor data with the assistance from state-of-the-art activity recognition algorithms. The tool visualizes high-sampling rate raw data and time-stamped annotations generated by existing activity recognition algorithms; the annotations can then be directly modified by the researchers to create their own, improved, labeled datasets.
Signaligner Pro is an open source tool available here to download. Please get in touch if you want to contribute to the code base here. Access to repo is available on request.
For more information, visit the detailed project page.
(Credit: Aditya Ponnada)
