Abstract
Human activity recognition (HAR) systems will be increasingly deployed in real-world environments and for long periods of time. This significantly challenges current approaches to HAR, which have to account for changes in activity routines, the evolution of situations, and sensing technologies. Driven by these challenges, in this paper, we argue the need to move beyond learning to lifelong machine learning—with the ability to incrementally and continuously adapt to changes in the environment being learned. We introduce a conceptual framework for lifelong machine learning to structure various relevant proposals in the area and identify some key research challenges that remain.
Highlights
Class EvolutionMost of the current HAR approaches follow a well-established methodology [14]:. Deployment: pre-define a closed set of activities of interest, and select a range of ambient and/or wearable sensors that can potentially detect the activities
Asking users “what are you doing at the moment” might not guarantee a relevant answer, which is different from annotation at the training phase where we have a set of predefined labels and ask the user to select whatever applies
The difficulty is that we might not be able to foresee what new activities users are doing, so we cannot provide them with the predefined label set
Summary
Most of the current HAR approaches follow a well-established methodology [14]:. Deployment: pre-define a closed set of activities of interest, and select a range of ambient and/or wearable sensors that can potentially detect the activities. Most of the current HAR approaches follow a well-established methodology [14]:. Deployment: pre-define a closed set of activities of interest, and select a range of ambient and/or wearable sensors that can potentially detect the activities. Model training: collect sensor data for a short period of time, annotate them with activity labels, and build a computational model to correlate sensor data with activities by defining expert knowledge or training a machine learning technique. Activity recognition: recognise current activities from real-time sensor data
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