An Energy Efficient Wearable Smart IoT System to Predict Cardiac Arrest

Table 2

Quantitative comparison of existing work based on different features.

Approach	Average Max HR	Approximate Accuracy	Average Max Sampling Rate	Number of Device (s) Used	Power Consumption in Watts
PatientsL-ikeMe [27]	160	90%	120	1	~ 500 mWatt
Daily Strength [28]	156	85%	110	1	N/A
Om-nio [29]	140	80%	100	1	N/A
Everyday Health [30]	144	85%	80	1	N/A
SEHMS [31]	155	78%	90	2	N/A
RMHM [32]	162	82%	140	2	N/A
PHM [33]	145	70%	150	1	N/A
Qardiocore [34]	135	78%	110	1	N/A
Maksimović [35]	155	85%	105	2	N/A
Stecker [36]	167	77%	130	1	N/A
Mancini [37]	151	87%	135	2	~ 600 mWatt
Sun [38]	160	75%	95	1	N/A
Communicore [39]	148	72%	150	1	N/A
Kavitha1 [40]	156	68%	155	1	N/A
Jagtap [41]	148	72%	145	2	N/A
Our Approach	135	95%	160	1	~ 444 mWatt