[Retracted] Improving Recognition of Overlapping Activities with Less Interclass Variations in Smart Homes through Clustering-Based Classification

Table 7

Confusion matrix on the Milan dataset without activities balancing using threefold in the combination of fuzzy C-means and ANN.

Acts	Slp	Tlt	Dsk	Dnr	Gbr	Kch	Mbr	Lh	Mr	Red	Tv	Mmd	Chr	Emd	Med
Slp	86.4						1.3		7.4	2.6	2.3
Tlt		79.5			5.4		15.1
Dsk			83.3	4.5			3.2						9.0
Dnr				83.8		10.3				1.2	4.7
Gbr		9.5			85.5		5.0
Kch						87.2	1.3					11.5
Mbr	1.0	13.7			2.0		83.3		3.0
Lh								98.0	2.0
Mr	5.0						2.4		83.0	2.6	5.4		1.6
Red									3.4	91.6	5.0
Tv						2.3			4.7	10.7	82.3
Mmd						10.0	4.8					78.2		7.0
Chr						5.0			15.0		4.0		76.0
Emd						8.3						21.3		70.4
Med					2.5	4.5	5.2	4.8				8.1		10.5	64.4

The columns represent the predicted activities, while the rows represent the actual activities. The score of overlapping activities is highlighted in bold. Key. acts: activities, Slp: sleeping, Tlt: bed to toilet, Dsk: desk activity, Dnr: dining room activity, Gbr: guest bathroom, Kch: kitchen activity, Mbr: master bathroom, Lh: leave home, Mr: master bedroom, Red: read, Tv: watch Tv, Mmd: morning medicine, Chr: chores, Emd: evening medicine, and Med: mediate.