Structure of the Design Space

TheDesign Space for Wearable Physiological Measurement Tools has been developed on the basis of ten semi-structured in-dpeth interviews with experts and consumers. In the following the structure of the design space will be presented in detail. All six dimensions including their 17 respective sub-dimensions will be explained and put in the context of physiology-sensing device usage.

Comfort of Attachments TheComfort of Attachmentis mainly important from the user perspective and summarizes physical and social discomforts associated with wearing a device. This dimension consists of four sub-dimensions referring to the underlying aspects being mentioned by the interviewees. Wearing Comfortis one of these; this sub-dimension refers to the pure agreeableness that is experienced, or not experienced when the sensing device is being attached to the body. Accordingly, there might be restrictions imposed on the wearer which is represented in the sub-dimensionDegree of Restrictiveness. For example, if the wearer wants to play an instrument which requires the hand that the device is attached to for playing, the sensing device could hamper the instrument and restrict the musician while playing. In such a case thePhysical Propertiesof a

Design Space for Physiological Measurement Tools

Mobility

Comfort of Attachment Robustness Data Accuracy Connectivity

Wearing Comfort Sensor Variety

Degree of RestrictivenessUnobtrusive- ness Invasiveness Degree of Preprocessing

Degree of Resolution Data Transmission Effort

Data Accessibility Effort

Software Reliability Trustworthi- nessDegree of Testedness Reliability Data Accessibility

Data Richness

Ease of Setup

Physical Properties Data Format

Figure 4.3: The Design Space for Physiological Measurement Tools in a mind-map-like visualization; including the six dimensions and its 17 sub-dimensions sorted by colors.

device, e.g. form factor, such as its color, shape, size, etc influence the perception and respectively the comfort. Another crucial factor is, if the attachment bearsInvasivenessor even worse, has harmful consequences for it’s wearer. While some sensing technologies might be cumbersome to wear, e.g. EEG hardware or chest straps, others might be less invasive when being put on easily accessible body locations, i.e. the wrist. Unobtrusiveness of a device was further found to be another selling point. In case the device communicates privacy-sensitive data, the wearer might not want to show that he or she is using such a device and therefore the criteria of an unobtrusive wearing is important.

Mobility The ability of being mobile when wearing the physiological sensing device has been summed up in this dimension. Another significant issue for researchers is the Ease of Setup of a particular device. In some application scenarios, the assessment of physiological signals might require an enormous effort to setup the hardware or software. Thus, this sub-dimension reflects a researcher’s perspective and neglects the consumer, since it has only high relevance when high-resolution or expensive equipment is used. Another sub-dimension affecting the perceivedMobility isRobustness; this refers to the resistance against external, and particularly environmental factors, such as water or heat.

Data Richness When it comes toData Richness, two different factors were considered, theSensor Varietyreferring to the amount of sensors being provided by a device and the quantity of the sensor data. The later is being taken up by the following two sub-dimensions. While theDegree of Resolutionmeans, for example the sampling frequency provided by the device, theDegree of Preprocessingcomprises the amount of, e.g. filters that is used to remove noise in the data or artifacts before the data can be accessed.

Data Accessibility For the dimensionData Accessibilitythree main factors were distinguished. Before the data can be accessed, there might be further equipment needed and therefore the sub-dimensionConnectivitymeans the ability to connect the physiological sensing device to other technologies, e.g. a laptop. In the second step after the data recording, there is often a transmission of data required. In practice, data has to be transferred from a server or a sensing device, which can range from very low to extremely highData Transmission Effort. As a final step often before accessing the data, there might be a special manufacturer’s software needed or a Software Developer Kit (SDK) needs to be installed resulting in a variation

of the Data Accessibility Effort to get granted access to the recorded physiological signals. An additional substantial factor for accessing data is theData Formatthat is being used for providing the physiological signals.

Since some products follow their own proprietary formats, others rely on a standard, such as Comma-Separated Values (CSV) files. Whether a standardized format is chosen can have severe consequences for the researcher wanting to access the data with the lowest effort as possible, what makes this issue a considerable sub-dimension.

Reliability What has been initially introduced as the dimensionData Reliability within previous work (cf., [98]) and found throughout the interviews, was simplified intoReliabilityto address a broader spectrum. By this aspect originally it has been referred to the newly added sub-dimension Data Accuracymeaning the degree to which the user of the data can be sure that no false values have been recorded. An additional understanding refers to theSoftware Reliabilitybeing another sub-dimension describing the susceptibility to errors.

Trustworthiness The dimension ofTrustworthinessemerged from consumers’, and particularly researchers’ considerations regarding the Degree of Testednessaffecting their decision criteria. If the device’s manufacturer was offering a huge amount of the device and had tested the device for certain criteria, e.g. reliability or invasiveness beforehand, this was clearly a confidence-building argument for purchasing such a device.