Research Article
Multimodal Sensor Data Integration for Indoor Positioning in Ambient-Assisted Living Environments
Table 1
Data distribution for each dataset.
| | Dataset | WAPs | Instances | Room 1 | Room 2 | Room 3 | Room 4 | Room 5 | Room 6 |
| | User 1 train | 45 | 4900 | 800 (16.33%) | 800 (16.33%) | 800 (16.33%) | 900 (18.35%) | 800 (16.33%) | 800 (16.33%) | | User 1 test | 29227 | 3909 (13.37%) | 8930 (30.55%) | 1214 (4.16%) | 1010 (3.46%) | 1870 (6.40%) | 12294 (42.06%) | | User 2 train | 115 | 3600 | 800 (22.22%) | 800 (22.22%) | 500 (13.89%) | 600 (16.67%) | 900 (25.00%) | | | User 2 test | 36718 | 31700 (86.33%) | 778 (2.12%) | 2790 (7.60%) | 0 (0.00%) | 1450 (3.95%) | | | User 3 train | 93 | 3350 | 750 (22.39%) | 1000 (29.84%) | 500 (14.93%) | 500 (14.93%) | 600 (17.91%) | | | User 3 test | 74731 | 27444 (36.72%) | 32065 (42.91%) | 5779 (7.73%) | 3488 (4.67%) | 5955 (7.97%) | | | User 4 train | 36 | 2600 | 600 (23.08%) | 600 (23.08%) | 400 (15.38%) | 600 (23.08%) | 400 (15.38%) | | | User 4 test | 8322 | 5134 (61.69%) | 328 (3.94%) | 1125 (13.52%) | 610 (7.33%) | 1125 (13.52%) | |
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