Minimum-Cost Sensor Channel Selection For Wearable Computing
CoRR(2024)
摘要
Sensor systems are constrained by design and finding top sensor channel(s)
for a given computational task is an important but hard problem. We define an
optimization framework and mathematically formulate the minimum-cost channel
selection problem. We then propose two novel algorithms of varying scope and
complexity to solve the optimization problem. Branch and bound channel
selection finds a globally optimal channel subset and the greedy channel
selection finds the best intermediate subset based on the value of a score
function. Proposed channel selection algorithms are conditioned with
performance as well as the cost of the channel subset. We evaluate both
algorithms on two publicly available time series datasets of human activity
recognition and mental task detection. Branch and bound channel selection
achieved a cost saving of up to 94.8
89.6
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