Long-Term Activity Recognition
Capturing what
you're doing, and when, for weeks
Activities play a key role in the way we structure our
lives. The type of activity, how it is performed, and with
whom, can reveal a person's intention, habit, fitness,
state of mind, and social connectedness; It is therefore
not surprising that a range of research fields, from
human-computer interaction to medical- and cognitive
sciences, display growing interest in automatic activity
recognition.
The purpose of this project is to formulate algorithms that
can be embedded in small wearable devices to process sensor
data in an energy- and memory-efficient way, while
capturing the essence of the human activity. The targeted
approach is to convert the sensor data immediately to
detected activities onboard the worn sensor, rather than
storing all raw sensor values, which would lead to high
energy and storage requirements on the wearable device.
Apart from algorithms, this project will produce dedicated
hardware and closely annotated benchmark data, and involves
researchers with vested interest in practical activity
detection applications.
Present-day sensors and microcontrollers already permit power efficient operation and wearable-sized sensors, mainly due to a growing prevalence of personal computing products. Novel algorithms to learn and detect human activities from on-body sensor data have been experimented on as well, though mostly for short spans of time.
This project aims at advancing this research by working
toward recognition of human activities over sustained
periods of time, using tiny wearable sensors that detect
motion- and posture characteristics, and map these to human
physical activities.
An example of the data captured by the HedgeHog sensor when wrist-worn: Fullscreen Example.