Achieving Accelerometer Wrist and Orientation Invariance in Physical Activity Classification via Domain Adaption

Purpose: Physical activity classifiers are typically trained on data obtained from sensors at a set orientation. Changes in this orientation (such as being on a different wrist) result in performance degradation. This work investigates a method to obtain sensor location and orientation invariance for classification of wrist-mounted accelerometry via a technique known as domain adaption. Methods: Data was gathered from 16 participants who wore accelerometers on both wrists. Physical activity classification models were created using data from each wrist and then used to predict activities when using data from the opposing wrist. Using subspace alignment domain adaption, this procedure was then repeated to align the training and testing data before the classification stage. Results: Prediction of activity when using data where the wearer’s wrist was incorrectly specified resulted in a significant (p = .01) decrease in performance of 12%. When using domain adaption this drop in performance became negligible (M difference < 1%, p = .73). Conclusion: Domain adaption is a valuable method for achieving accurate physical activity classification independent of sensor orientation in wrist-worn accelerometry.

Orientation-invariance of individual differences in three face processing tasks

Numerous studies have reported impairments in perception and recognition, and, particularly, in part-integration of faces following picture-plane inversion. Whether these findings support the notion that inversion changes face processing qualitatively remains a topic of debate. To examine whether associations and dissociations of the human face processing ability depend on stimulus orientation, we measured face recognition with the Cambridge Face Memory Test (CFMT), along with experimental tests of face perception and selective attention to faces and non-face objects in a sample of 314 participants. Results showed strong inversion effects for all face-related tasks, and modest ones for non-face objects. Individual differences analysis revealed that the CFMT shared common variance with face perception and face-selective attention, however, independent of orientation. Regardless of whether predictor and criterion had same or different orientation, face recognition was best predicted by the same test battery. Principal component decomposition revealed a common factor for face recognition and face perception, a second common factor for face recognition and face-selective attention, and two unique factors. The patterns of factor loadings were nearly identical for upright and inverted presentation. These results indicate orientation-invariance of common variance in three domains of face processing. Since inversion impaired performance, but did not affect domain-related associations and dissociations, the findings suggest process-specific but orientation-general mechanisms. Specific limitations by constraints of individual differences analysis and test selection are discussed.