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Fig. 4 | International Journal of Behavioral Nutrition and Physical Activity

Fig. 4

From: Using functional principal component analysis (FPCA) to quantify sitting patterns derived from wearable sensors

Fig. 4

Example fitting profiles in different participants on different days of MFPCA curves tracing the observed VM counts/minute (black dots) within sitting bouts by adding one element at a time based on model Eq. 1: 1. \(\mu (t)\); 2. \(\mu (t)+ \eta _j(t)\); 3. \(\mu (t)+ \eta _j(t)+ \sum _{k=1}^{2}\xi _{ik}\phi _k^{(1)}(t)\); 4. \(\mu (t)+ \eta _j(t)+ \sum _{k=1}^{2}\xi _{ik}\phi _k^{(1)}(t)+ \sum _{l=1}^{6}\zeta _{ijl}\phi _l^{(2)}(t)\). a was the same participant-day from Fig. 1 where a majority of VM counts were below 1000 cpm with notable size of those close to 0; b was a different participant on a different day where a large number of VM counts were still above 1000 cpm and fewer VM counts were close to 0

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