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Table 7 Comparison of the results on training- and validation set (when the dataset contains two trends both trends are evaluated separately)

From: Developing a system that can automatically detect health changes using transfer times of older adults

Training scenariosa

Validation scenariosb

Detection Rate (DR)

T r SU

100 %

V SU

100 %

T r US

100 %

V US

100 %

T r SUS

 

V SUS

 

S →U

100 %

S →U

100 %

U →S

100 %

U →S

100 %

T r USU

 

V USU

 

U →S

100 %

S →U

100 %

S →U

100 %

U →S

100 %

Average Run Length (ARL)

T r SU

8.05±3.62

V SU

7.70±3.34

T r US

12.95±4.31

V US

10.95±3.59

T r SUS

 

V SUS

 

S→U

7.65±4.10

S →U

8.35±3.59

U →S

11±4.30

U →S

12.45±5.25

T r USU

 

V USU

 

U →S

10.15±5.96

U →S

10±3.54

S →U

8±5.58

S →U

8.10±2.88

Average number of false alarms per week (FPR)

T r S

0.15±0.17

V S

0.16±0.07

T r U

0.11±0.07

V U

0.14±0.06

T r SU

0.20±0.10

V SU

0.20±0.07

T r US

0.18±0.07

V US

0.17±0.07

T r SUS

0.23±0.08

V SUS

0.20±0.08

T r USU

0.23±0.07

V USU

0.20±0.08

  1. Notes
  2. aTraining simulation scenarios as described in Table 3
  3. bValidation simulation scenarios as described in Table 4