The purpose of this project is to develop a model that is capable of recognizing daily basic human activities under real-world conditions, using data collected by a waist-mounted triaxial accelerometer and gyroscope built into a cellphone (in our study, a Samsung Galaxy S II). Activity recognition is formulated as a supervised classification problem, whose data is obtained via an experiment having 30 human subjects perform each of the activities. Our classification models have been trained and tested with data of subjects performing the following six physical activity patterns: Walking, Walking up-stairs, Walking down-stairs, Sitting, Standing and Laying down.
Herforth, N. (2021). Human Activity Recognition by Wearable Sensors. Afribary. Retrieved from https://track.afribary.com/works/human-activity-recognition-by-wearable-sensors
Herforth, Nicolai "Human Activity Recognition by Wearable Sensors" Afribary. Afribary, 14 Jan. 2021, https://track.afribary.com/works/human-activity-recognition-by-wearable-sensors. Accessed 20 Nov. 2024.
Herforth, Nicolai . "Human Activity Recognition by Wearable Sensors". Afribary, Afribary, 14 Jan. 2021. Web. 20 Nov. 2024. < https://track.afribary.com/works/human-activity-recognition-by-wearable-sensors >.
Herforth, Nicolai . "Human Activity Recognition by Wearable Sensors" Afribary (2021). Accessed November 20, 2024. https://track.afribary.com/works/human-activity-recognition-by-wearable-sensors