Mobile Sensing Around the Globe: Considerations for Cross-Cultural Research

The ubiquity of mobile devices allows researchers to assess people’s real-life behaviors objectively, unobtrusively, and with high temporal resolution. As a result, psychological mobile sensing research has grown rapidly. However, only very few cross-cultural mobile sensing studies have been conducted to date. In addition, existing multi-country studies often fail to acknowledge or examine possible cross-cultural differences. In this chapter, we illustrate biases that can occur when conducting cross-cultural mobile sensing studies. Such biases can relate to measurement, construct, sample, device type, user practices, and environmental factors. We also propose mitigation strategies to minimize these biases, such as the use of informants with expertise in local culture, the development of cross-culturally comparable instruments, the use of culture-specific recruiting strategies and incentives, and rigorous reporting standards regarding the generalizability of research findings. We hope to inspire rigorous comparative research to establish and refine mobile sensing methodologies for cross-cultural psychology.

Objective Measurement of Hyperactivity Using Mobile Sensing and Machine Learning (Preprint)

UNSTRUCTURED Although hyperactivity is a core symptom of ADHD, there are no objective measures that are widely used in clinical settings. We describe the development of a smartwatch application to measure hyperactivity in school-age children. The LemurDx prototype is a software system for smartwatches that uses wearable sensor technology and machine learning (ML) to measure hyperactivity, with the goal of differentiating children with ADHD combined presentation or predominantly hyperactive/impulsive presentation from children with typical levels of activity. In this pilot study, we recruited 30 children (ages 6-11) to wear the smartwatch with the LemurDx app for two days. Parents also provided activity labels for 30-minute intervals to help train the algorithm. Half the sample had ADHD combined presentation or predominantly hyperactive/impulsive presentation (n = 15) and half were healthy controls (n = 15). Results indicated high usability scores and an overall diagnostic accuracy of .89 (sensitivity = .93; specificity = .86) when the motion sensor output was paired with the activity labels, suggesting that state-of-the-art sensors and ML may provide a promising avenue for the objective measurement of hyperactivity.

Integrated enhanced Raman scattering: a review

The demand for effective, real-time environmental monitoring and for customized point-of-care (PoC) health, requires the ability to detect low molecular concentrations, using portable, reliable and cost-effective devices. However, traditional techniques often require time consuming, highly technical and laborious sample preparations, as well as expensive, slow and bulky instrumentation that needs to be supervised by laboratory technicians. Consequently, fast, compact, self-sufficient, reusable and cost-effective lab-on-a-chip (LOC) devices, which can perform all the required tasks and can then upload the data to portable devices, would revolutionize any mobile sensing application by bringing the testing device to the field or to the patient. Integrated enhanced Raman scattering devices are the most promising platform to accomplish this vision and to become the basic architecture for future universal molecular sensors and hence an artificial optical nose. Here we are reviewing the latest theoretical and experimental work along this direction.

Mobile Sensing with Smart Wearables of the Physical Context of Distance Learning Students to Consider Its Effects on Learning

Research shows that various contextual factors can have an impact on learning. Some of these factors can originate from the physical learning environment (PLE) in this regard. When learning from home, learners have to organize their PLE by themselves. This paper is concerned with identifying, measuring, and collecting factors from the PLE that may affect learning using mobile sensing. More specifically, this paper first investigates which factors from the PLE can affect distance learning. The results identify nine types of factors from the PLE associated with cognitive, physiological, and affective effects on learning. Subsequently, this paper examines which instruments can be used to measure the investigated factors. The results highlight several methods involving smart wearables (SWs) to measure these factors from PLEs successfully. Third, this paper explores how software infrastructure can be designed to measure, collect, and process the identified multimodal data from and about the PLE by utilizing mobile sensing. The design and implementation of the Edutex software infrastructure described in this paper will enable learning analytics stakeholders to use data from and about the learners’ physical contexts. Edutex achieves this by utilizing sensor data from smartphones and smartwatches, in addition to response data from experience samples and questionnaires from learners’ smartwatches. Finally, this paper evaluates to what extent the developed infrastructure can provide relevant information about the learning context in a field study with 10 participants. The evaluation demonstrates how the software infrastructure can contextualize multimodal sensor data, such as lighting, ambient noise, and location, with user responses in a reliable, efficient, and protected manner.