  Sensor-based human activity recognition (HAR), i.e., the ability to discover
human daily activity patterns from wearable or embedded sensors, is a key
enabler for many real-world applications in smart homes, personal healthcare,
and urban planning. However, with an increasing number of applications being
deployed, an important question arises: how can a HAR system autonomously learn
new activities over a long period of time without being re-engineered from
scratch? This problem is known as continual learning and has been particularly
popular in the domain of computer vision, where several techniques to attack it
have been developed. This paper aims to assess to what extent such continual
learning techniques can be applied to the HAR domain. To this end, we propose a
general framework to evaluate the performance of such techniques on various
types of commonly used HAR datasets. We then present a comprehensive empirical
analysis of their computational cost and effectiveness of tackling HAR-specific
challenges (i.e., sensor noise and labels' scarcity). The presented results
uncover useful insights on their applicability and suggest future research
directions for HAR systems. Our code, models and data are available at
https://github.com/srvCodes/continual-learning-benchmark.
