Dr Genovefa Kefalidou and Dr Neil de Joux of the University of Nottingham are working hard at testing different smartwatch’s usability and accuracy. The technology will then be used to measure passenger’s stress levels at various stages in their travel journey. The data collected will help researchers to decide at which stage in the journey passenger experience needs to be improved.
In order to ensure the devices are accurate, the University of Nottingham Human Factors researchers have been wearing the watches while carrying out their day-to-day tasks. Data is being collected throughout the day from when they are sleeping to the time they spend at work or performing cardiovascular activity.
As can be seen in all of the following figures, four different physiological measurements are captured through the use of the E4 wristband during the course of different days for different University of Nottingham researchers.
The physiological measurements captured are:
1) Skin conductance (measuring levels of sweat, denoted by the EDA measurements below)
2) BVP (blood volume-this metric is used for calculating HRV (heart-rate variability)),
3) Accelerometer (for capturing movement)
4) HR (heart rate – beats per minute).
The first figure demonstrates the measurements taken from an individual while asleep. The second image shows an individual’s measurements during one of their routine working days. The third picture shows physiological measurements of an individual while exercising at a gym.
Figure 1: The individual in this case is sleeping. As can be seen in the figure, while asleep the measurements are following a flat pattern apart from a couple of cases in the early morning hours (EDA measurement only). As soon as the individual wakes up (see red line time stamp), the Accelerometer and HR recordings spike upwards.
Figure 2: The activity shown before the time stamp is characterised by a rise in skin conductance, accelerometer and heart rate as the participant is walking to work. Following this time stamp, all measures become more balanced as the user settles in and acclimatises to their work day.
Figure 3: This image shows the same readings as before, taken as the user is participating in a strenuous gym session lifting heavy weights (specifically squats). It shows fluctuations in readings as the user engages in activity and recovers throughout the workout.