Why the study?
Does implementing real-time emotion detection on a wireless sensor node reduce power consumption compared to transmitting raw EEG data?
Does implementing real-time emotion detection on a wireless sensor node reduce power consumption compared to transmitting raw EEG data?
Implementing real-time emotion detection algorithms directly on a wireless sensor node significantly reduces power consumption and prolongs battery life compared to transmitting raw EEG data.
May extend wearable EEG monitoring duration; leaves open clinical validation for emotion detection applications.
Recent advances in portable and wearable electroencephalograph (EEG) devices has raised the need to detect emotions in real time for applications such as wellbeing monitoring, gaming and social networking. A number of researchers have reported real time emotion detection systems implemented on a computer. This study advances these efforts by implementing a real time emotion detection system on a wirelesses sensor node with minimal hardware resources (256 kb of flash memory and 16 MHz processing speed) suitable for integration in a wearable wireless sensor node. The experimental results demonstrate that detecting emotions within the sensor node using suitable algorithms prolong the battery life by 5 days (38%) and by 39 days at an emotion detection rate of 2 and 60 s, respectively, as compared with transmitting the raw EEG data wirelessly. This also reduces the length of packets transmitted which directly minimises the packet error rate and the power that would be consumed because of retransmission of these erroneous packets.
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Matiko et al. (2014) studied this question.
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