working environment, in which the participants were asked to perform various tasks on a tablet computer.
Self-reported subjective CW was questioned directly after each task. These were used as ground truth information. In order to predict CW, EDA and HR were recorded.
At first, it could be shown that the sensor readings are comparable to those taken from laboratory reference. In total, 42 features were calculated on the basis of the EDA and HR sensor. Most significant features and their ideal window sizes and overlaps were determined with an initial spot-resting approach based on 10-fold CV DTs. The identified sparse feature subset contains 10 features, which include 5 EDA-based, and 5 HR- or HRV-based features. The feature subset was then evaluated by comparing the accuracy of multiple well-established ML methods.
By employing the developed and applied models, it was shown that it is possible to distinguish between stress-free and stressful tasks. Furthermore, the fine-grained estimation with 5 levels (classes) is possible. In conclusion, a classification accuracy of 94.6 % for the binary CW estimation and an accuracy of 72.6 % for the fine-grained estimation was found.
Moreover, it was shown that these features can be calculated on a short-term basis.
This is a mandatory requirement in order to set up an adaptive assistive system, which is capable of balancing the complexity of a given task accordingly to the user’s cognitive capacity.
It was furthermore observed that the subjective estimation of CW, which serves as ground truth, is affected by uncertainty. Similar results were described independently to this work in later published studies[155]. This again highlights the necessity to compare subjective and objective markers such as the cortisol level in future work.
In this regard, preliminary work on the integration of chemical sensors in WBS has already been presented[26, 161]. However, before these and comparable sensors can be reliably integrated into WBS, several challenges still have to be solved, which include long-term stability and device-skin adherence[161].
Apart from these potentials for future work, the added value of WBS is already evident today. In psychology, the so-calledexperience samplingmethod has been discussed for some years[70]. A similar concept is found withecological momentary assessments [218]. Both aim to capture subjective impressions or emotions as directly as possible.
Today, questionnaires are often no longer designed on paper and retrospectives tools but are collected as directly as possible, e.g. using a smartphone (or as in this work a tablet computer). In this way, common biases e.g. arecall biasorrecency effect, could be counteracted11. Although using a smartphone, a question (as part of a questionnaire) can be asked right after an event, the measurement of physiological parameters takes
11These effects are given here for the sake of illustration. Withrecall bias, false memory reports are denoted in general. Therecency effectdescribes a phenomena where the last remembered information is more present in the short-time memory compared to an information which was remembered earlier. The list of memory or cognitive biases in psychology is long and an independent branch of research. An overview can be found in[60].
4.4 Conclusion and Future Work
place immediately, which is parallel to an event. Using WBS thus allows gathering insight into the temporal dynamics of emotions and subjective perception in particular.
The use of WBS forms the basis for new research methods in this field, the applicability, and effectiveness of which was demonstrated in this chapter. In view of decreasing costs for mobile computing and wearable sensors, a further spread can be expected. For future work, this means that WBS will open up new research questions. It is moreover conceivable to use the sensors as indirect markers. In this sense, spontaneous changes of activity, such as the increase of HR or sudden onset of movement, could be used as triggers to initiate user surveys. These triggers could then be useful to look at future psychological questions more closely and from a new perspective, i.e. under the view of dynamics in activity behavior.
Table 4.5:Selected methods used for feature extraction.
Signal Category Function Definition Heart rate
(HR)
time average µ= 1nPni=1xi standard
deviation
σ=qn−11 Pni=1(xi−µ)2 Heart rate
variability (HRV)
time average 1/nPn
i=1ai
temporal skew 1/nPn
i=1(xi−µ)3 kurtosis 1/nPn
i=1(xi−µ)4 heart rate
variability
NN50 Pn−1
i=1(xi−xi+1>.05)
RMSSD q
1/nPn
i=1(xi−xi+1)2 SDSD σ((x1−x2). . .(xn−1−xn))
SD1 p
.5·S DS D2
SD2 Æ
(2·S DS D2)−(.5·σ2(x)) SD12 S D1/S D2
spectral VLF energy 0.00 Hz to 0.04 Hz LF energy 0.04 Hz to 0.15 Hz HF energy 0.15 Hz to 0.40 Hz nLF normalized energy (L F/L F+H F) nHF normalized energy (H F/L F+H F) LF/HF L F/H F
geometric TRI relative frequency of mode TINN triangular fit to histogram rrHRV median Euclidean distance of
suc-cessive RR towards their mean
Electrodermal-activity (EDA, SCR, SCL)
geometric (peak)
count number of peaks
amplitude amplitude of peak (max) duration distance between the two
mini-mums surrounding a peak area integral between the two
minimums surrounding a peak
5 Monitoring Physical Activity
In the course of this chapter, the topic of physical activity (PA) estimation utilizing WBS is addressed. The focus is on the application in a professional context, i.e. firefighting.
After a brief motivation (sec. 5.1), existing reference and alternative methods to measure PA (or EE) are outlined (sec. 5.2). Thereafter, recent multi-modal approaches to estimate EE utilizing WBS are introduced (sec. 5.3). Additionally, aspects of the calibration of WBS are addressed (sec. 5.4). Parts of these chapters were previously published in[272, 275]. In the following, a multi-modal and robust model to estimate the PA of firefighters is introduced (sec. 5.5).
5.1 Background and Motivation
Quantifying PA is practiced both in private and in professional contexts[76]. Pro-fessional examples can be found with soldiers, athletes[140], or ergonomists[179] (sec. 2.2.1). Today, fitness trackers or activity trackers are used in private for self-measurement (an extreme can be found in the quantified-self movement)[5].
While private individuals and athletes may initially have an interest in recording their activities or performance out of a selfish interest, the relevance for the general public results from the positive health effects of PA. This is because PA and cardio-respiratory fitness are regarded as key factors in avoiding numerous diseases of affluence[179, 222, 237]. These include obesity, type 2 diabetes, cardiovascular diseases, or mental health[247]. An active way of life thus promotes the health of each individual and therefore is of major relevance towards public health[240].
The relevance of PA goes beyond private life and is just as important for other dimensions of life, such as work. However, different user groups also have different expectations and requirements. For instance, validity and cost, but also comfort and functionality, are dimensions that conflict with each other[119]. Here lies the basic idea of the design and application of WBS and other wearable devices, which is to construct sensors, technical systems, and algorithms in such a way that these conflicting expectations are optimized in parallel. Regarding costs and time, WBSs already have an advantage over laboratory methods. Specifying and maximizing validity, on the other hand, is the topic of current research work[9, 78].
Estimating PA utilizing WBS is a widespread technique with a long history (sec. 2.2.1).
The approaches used have evolved from simple linear methods based solely on ac-celerometer data[167]or heart rate[220]towards more sophisticated methods fusing multiple sensor data using advanced non-linear ML methods[72]. Furthermore, recent
work engaged architectures that use several activity-specific models rather than develop one single stand-alone model[12, 72].
Since then, quantifying one’s own PA has emerged into everyday life, having fitness trackers or specificAppsfor smartphones and smartwatches available on the market [185]. However, for applications apart from being a leisure activity, the validity of the predicted outcome is more crucial and still a relevant research topic[172].