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Sensor devices have been increasingly used in engineering and health studiesrecently, and the captured multi-dimensional activity and vital sign signalscan be studied in association with health outcomes to inform public health. Thecommon approach is the scalar-on-function regression model, in which healthoutcomes are the scalar responses while high-dimensional sensor signals are thefunctional covariates, but how to effectively interpret results becomesdifficult. In this study, we propose a new Functional Adaptive Double-Sparsity(FadDoS) estimator based on functional regularization of sparse group lassowith multiple functional predictors, which can achieve global sparsity viafunctional variable selection and local sparsity via zero-subintervalidentification within coefficient functions. We prove that the FadDoS estimatorconverges at a bounded rate and satisfies the oracle property under mildconditions. Extensive simulation studies confirm the theoretical properties andexhibit excellent performances compared to existing approaches. Application toa Kinect sensor study that utilized an advanced motion sensing device trackinghuman multiple joint movements and conducted among community-dwelling elderlydemonstrates how the FadDoS estimator can effectively characterize the detailedassociation between joint movements and physical health assessments. Theproposed method is not only effective in Kinect sensor analysis but alsoapplicable to broader fields, where multi-dimensional sensor signals arecollected simultaneously, to expand the use of sensor devices in health studiesand facilitate sensor data analysis.

Functional Adaptive Double-Sparsity Estimator for Functional Linear Regression Model with Multiple Functional Covariates