Binary classification results for each classifier (daily prediction).

Binary classification results for each classifier (daily prediction).</p

Archetype for activity tracking.

This daily report illustrates real data of an old person in free-living conditions. The goal of this graphical representation of these features is on the one hand, to provide necessary information on the evolution of health conditions for seniors who would become the actors of their own health, and on the other hand to help the communication between patients and clinicians. The first figure (blue bars) represents the activity rate (in %) during a specific day. The second row shows, on the one hand, the number of steps and the number of burned calories, and, on the other hand, the sleep pattern. Afterwards, the rate and duration of periodic movements are illustrated (third row). Finally, the localized moments during which the subject has used the lift/stairs are pictured in the last graph. Explicitly, the corresponding subject was highly active between 9 am and 12 pm. He was mostly inactive at night (while he was sleeping). Moreover, the sleep patterns are illustrated, where the cycles are pictured in green, and the interruptions in red. Two types of interruptions are detected: (i) small ones resulting from rotation or change of positions, and (ii) relatively long ones resulting from higher activity levels, where the subject is active and moving. The latter is represented by the second and fourth interruptions in the report, when the subject woke up and went to the toilet. This result is coherent with the activity rate diagram (blue bars), where it is shown that the subject was active between 1-2 am, and 4-5 am. Then he went into deep sleep again, since the fifth cycle is large. Furthermore, 3.3% of the subject’s activities were periodic between 11 am and 12 pm (orange bars). Meanwhile, his periodic movements never exceeded 25 minutes (yellow bars). Finally, the subject took the lift upward four times (one of them localized at 7:44 pm) and downward three times, and he descended the stairs at 12:13 pm. In our solution, 3 meters correspond to one floor. (PDF)</p

Different statistics representing the extracted health measurements of (a) frail people and (b) healthy (non-frail) old people.

Different statistics representing the extracted health measurements of (a) frail people and (b) healthy (non-frail) old people.</p

Demographic details of the study cohort.

Demographic details of the study cohort.</p

Wearable device.

The sensing unit placement (body trunk in yellow), with the corresponding printed circuit board and the 3D orientation of the accelerometer.</p

Binary classification results for three classifiers following a 5-day prediction.

Binary classification results for three classifiers following a 5-day prediction.</p

Box charts of extracted features.

The set of box charts showing the data distribution of each feature (x-axis) following both populations (Robust vs Frail).</p

Altitude via barometric signals.

The change in altitude while using stairs (tan(θS) ≈ 0.23m/s) and while using lifts (tan(θL) ≈ 0.66m/s).</p

MET equivalents depending on activity class and intensity.

MET equivalents depending on activity class and intensity.</p

Principal component analysis (2D).

Data distribution of both populations following two principal components, with the SVM boundary in dashed lines, after being scaled using (a) z-score formula and (b) sigmoidal function.</p