You can apply machine learning to detect activities by reading and processing sensor data in this regard.
Human activity recognition (HAR) aims to classify a person’s actions from a series of measurements captured by sensors.
Nowadays, collecting this type of data is not an arduous task. With the growth of the Internet of Things, almost everyone has some gadget that monitors their movements. It can be a smartwatch, a pulsometer, or even a smartphone.
Usually, this is performed following a fixed-length sliding window approach for feature extraction. Here two parameters need to be fixed: the window size and the shift.
These are some of the data you could use:
- Body acceleration.
- Gravity acceleration.
- Body angular speed.
- Body angular acceleration.
The machine learning model used for activity recognition relies on top of the devices’ available sensors.
However, analyzing this data can be a big challenge. Indeed, human activities are complex, and there are differences between individuals.
Activity recognition is the basis for the development of many potential applications in health, wellness, or sports:
Analyze the activity of a person from the information collected by different devices.
DISCOVER ACTIVITY PATTERNS
Discover which are the variables that determine which activity a person is doing.
Calculate a predictive model that can recognize a person’s activity from the signals received by the sensors.
Design individualized exercise tables to improve the health of a person.
Neural networks are the perfect algorithms to determine a person’s physical activity. This is due to their ability to recognize the patterns behind the data.
The following graph illustrates a neural network that classifies different activities using smartphone data.
Human activity recognition has a wide range of uses because of its impact on wellbeing.
It is becoming a fundamental tool in healthcare solutions such as preventing obesity or caring for elderly persons.
- Deep learning for sensor-based activity recognition: A survey.
- Chernbumroong, S., Cang, S., Atkins, A., & Yu, H. (2013). Elderly activities recognition and classification for applications in assisted living. Expert Systems with Applications, 40(5), 1662-1674.
- Anguita, D., Ghio, A., Oneto, L., Parra, X., & Reyes-Ortiz, J. L. (2013, April). A public domain dataset for human activity recognition using smartphones. In Esann.
- Mannini, A., & Sabatini, A. M. (2010). Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors, 10(2), 1154-1175.