How we trained Fitbit’s Body Response feature to detect stress

Fitbit’s stress detection technology is designed to measure a user’s physiological response to stress and help them manage it. Initially, Fitbit developed the Stress Management Score algorithm which calculates a person’s resilience to stress based on physiological, sleep and activity data collected over the previous week. In addition, the sEDA sensor on the Fitbit Sense smartwatch provides spot checks for stress levels by measuring micro-sweat levels on the palm of the hand.

The new Body Response feature on the Fitbit Sense 2 incorporates a cEDA sensor that tracks stress throughout the day. The algorithm uses a machine learning algorithm to monitor heart rate, heart rate variability, skin temperature, and sweat levels for sudden changes that indicate stress. The feature sends notifications to prompt the user to take action, such as guided breathing or walking, to reduce stress levels.

The body’s response to stressors causes changes in hormonal and physiological functioning, known as autonomic arousal. These changes can also occur during positive or exciting events such as going on a first date or hosting a big party. By logging their mood on their Fitbit device or app, users can help to differentiate between stress and positive arousal.

The algorithm learns from the user’s data over the first month to establish a baseline for more accurate detection of acute changes from that baseline. During exercise, the algorithm is disabled as the body signals are likely caused by the workout rather than stress.

Overall, Fitbit’s stress detection technology aims to help users manage stress by detecting and prompting them to take action to reduce stress levels. By incorporating machine learning algorithms and continuous tracking, Fitbit continues to innovate their technology to improve wellbeing.


There are no comments yet.

Leave a comment