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An accelerometer is a device that measures force due to gravity or a change in speed or direction of travel. This paper describes accelerometers and their application in other disciplines and, by way of an example, explores the utility of accelerometers for studying aggression. We end with a discussion of additional ways accelerometers might be used in group processes research.

We first review the use of accelerometers in other disciplines. We then present the results of four studies that demonstrate the use of accelerometers to measure aggression. Study 1 establishes the measure’s concurrent validity. Study 2 concerns its stability and representative reliability. Study 3 seeks to establish the measure’s predictive validity by associating it with an existing measure. Study 4 demonstrates the ability of accelerometers to address a sociological research question.

In Studies 1 and 2, we find that accelerometers can be used to differentiate between distinct levels of aggression. In Study 3, we find that men’s average peak acceleration correlates with a previously validated measure of aggression. Study 4 uses accelerometers to reproduce a well-established finding in the aggression literature.

We conclude that accelerometers are a flexible tool for group processes’ researchers and social scientists more broadly. Our findings should prove useful to social scientists interested in measuring aggression or in employing accelerometers in their work.

Purpose — The Department for Transport's 2011 GPS National Travel Survey (NTS) pilot study investigated whether personal GPS devices and automated data processing could be used in place of the 7-day paper diary. Using GPS technology could reduce the relatively high burden that the diary places upon respondents, reduce costs and improve data quality.

Design/methodology/approach — Data was collected from c.900 respondents. Practical changes were made to the existing methodology where necessary, including the collection of information to support data processing. Processing was undertaken using the University of Eindhoven's Trace Annotator. Results from the GPS pilot were then compared to those from the main NTS diaries for the same period.

Findings — There were no insurmountable problems using GPS devices to collect data; however, the processed GPS data did not resemble the diary outputs, making GPS unsuitable for the NTS. The GPS data produced fewer and longer trips than the diary data. The purpose of a quarter of the GPS trips was unclear, and a disproportionate share started and ended at home.

Research limitations — Further work to manually inspect trips identified via validation as unfeasible and subsequently refine the processing algorithms would have been desirable had time permitted. GPS data processing may have been hindered by missing GPS data, particularly in the case of rail travel.

Originality/value — This research used an accelerometer-equipped GPS device to better predict the method of travel. It also combined addresses that respondents reported having visited during the travel week with GIS data to code the purpose of trips without using a post-processing prompted-recall survey.

The mid-1990s marked a paradigm shift in the way physical activity is promoted, and walking is now considered the most suitable type of physical activity for widespread promotion. Accurate measurement underpins public health practice, hence the aims of this chapter are to: (1) provide a typology for the measurement of walking; (2) review methods to assess walking; (3) present challenges in defining walking measures; (4) identify issues in selecting instruments for the evaluation of walking and (5) discuss current efforts to overcome measurement challenges and methodological limitations. The taxonomy of walking indicates that secondary purpose walking is a more complex set of behaviours than primary purpose walks. It has many purposes and no specific domain or intensity, may lack regularity, and therefore poses greater measurement challenges. Objective measurement methods, such as accelerometers, pedometers, smartphones and other electronic devices, have shown good approximation for walking energy expenditure, but are indirect methods of walking assessment. Global Positioning System technology, the ‘Smartmat’ and radio-frequency identification tags are potential objective methods that can distinguish walkers, but also require complex analysis, are costly, and still need their measurement properties corroborated. Subjective direct methods, such as questionnaires, diaries and direct observation, provide the richest information on walking, especially short-term diaries, such as trip records and time use records, and are particularly useful for assessing secondary purpose walking. A unifying measure for health research, surveillance and health promotion would strongly advance the understanding of the impact of walking on health.

Health scientists and urban planners have long been interested in the influence that the built environment has on the physical activities in which we engage, the environmental hazards we face, the kinds of amenities we enjoy, and the resulting impacts on our health. However, it is widely recognized that the extent of this influence, and the specific cause-and-effect relationships that exist, are still relatively unclear. Recent reviews highlight the need for more individual-level data on daily activities (especially physical activity) over long periods of time linked spatially to real-world characteristics of the built environment in diverse settings, along with a wide range of personal mediating variables. While capturing objective data on the built environment has benefited from wide-scale availability of detailed land use and transport network databases, the same cannot be said of human activity. A more diverse history of data collection methods exists for such activity and continues to evolve owing to a variety of quickly emerging wearable sensor technologies. At present, no “gold standard” method has emerged for assessing physical activity type and intensity under the real-world conditions of the built environment; in fact, most methods have barely been tested outside of the laboratory, and those that have tend to experience significant drops in accuracy and reliability. This paper provides a review of these diverse methods and emerging technologies, including biochemical, self-report, direct observation, passive motion detection, and integrated approaches. Based on this review and current needs, an integrated three-tiered methodology is proposed, including: (1) passive location tracking (e.g., using global positioning systems); (2) passive motion/biometric tracking (e.g., using accelerometers); and (3) limited self-reporting (e.g., using prompted recall diaries). Key development issues are highlighted, including the need for proper validation and automated activity-detection algorithms. The paper ends with a look at some of the key lessons learned and new opportunities that have emerged at the crossroads of urban studies and health sciences.

We do have a vision for a world in which people can walk to shops, school, friends' homes, or transit stations; in which they can mingle with their neighbors and admire trees, plants, and waterways; in which the air and water are clean; and in which there are parks and play areas for children, gathering spots for teens and the elderly, and convenient work and recreation places for the rest of us. (Frumkin, Frank, & Jackson, 2004, p. xvii)

Experience sampling methods (ESM) have enabled researchers to capture intensive longitudinal data and how worker well-being changes over time. The conceptual advances in understanding the variability of well-being are discussed. These emerging forms in the literature include affective inertia, affective variability, affective reactivity, and density distributions. While most ESM research has relied on the active provision of data by participants (i.e., self-reports), technological advances have enabled different forms of passive sensing that are useful for assessing and tracking well-being and its contextual factors. These include accelerometer data, location data, and physiological data. The strengths and weaknesses of passively sensed data and future ways forward are discussed, where the use of both active and passive forms of ESM data in the assessment and promotion of worker well-being is expected.

The overarching aim of our research is the social and environmental sustainability of cities, with a focus on ensuring the rights and needs of the children who live in them are considered in policy and planning arenas. How do we, as researchers, work ethically and effectively with children to foreground their voices and produce robust evidence to inform policies and processes which promote their wellbeing in child-friendly cities, and in line with Sustainable Development Goals? Children have the right to be heard, and their views taken seriously, in policy and planning arenas. Conducting ethical and effective child-centred research requires balancing considerations of children’s rights to genuine participation and their rights to protection at all stages of the research process. This balance requires methodological flexibility and a situated ethical approach, where researchers and participants together determine appropriate research pathways. In this chapter, the authors reflect on ethical and methodological insights gained during a decade of conducting urban-related research with children. The various projects used different methods and provided different lessons; but common to all was an understanding of the importance of relationship-building, of supportive and engaged adults, and of methods which were respectful, age/culturally appropriate and ‘fit for purpose’. These factors are crucial to ethically enable the foregrounding of children’s voices, the collection of robust data and effective dissemination of research with children.

Evaluating pain and discomfort in animals is difficult at best. Veterinarians believe however, that they can establish a proxy for estimating levels of pain and discomfort in canines by observing variations in their activity levels. Sufficient research has been conducted to justify this assertion, but little has been conducted to analyze the volumes of activity data collected. We present the first of a series of analyses aimed at ultimately presenting an effective predictive tool for canine pain and discomfort levels. In this chapter, we perform analyses on a dataset of normal (control) dogs, containing almost 3 million records. The forecasting analyses incorporated multiple polynomial regression models with transcendental transformations and ARIMA models to provide effective determination and prediction of baseline normal canine activity levels.

This is the third in a series of papers aimed at providing models effective in predicting the degree of pain and discomfort in canines. The first two papers provided benchmarking and examination of dogs suffering from osteoarthritis (OA). In this chapter, we extend the study to include dogs suffering from OA, sarcoma, and oral mucositis (a side effect of chemotherapy and radiation treatments). The R programming language and SAS JMP are used to clean data, generate ANOVA, LSR regression, decision tree, and nominal logistic regression models to predict changes in activity levels associated with the progression of arthritis. The predictive models provide a diagnostic basis for determining the degree of disease in a dog (based on demographics and activity levels) and provide forecasts that assist in establishing appropriate medication dosages for suffering dogs.

Purpose: This chapter describes the design and implementation, at the computer hardware and software level, of the Greggg robot. Greggg is a scalable high performance, low cost hospitality robot constructed from off-the-shelf parts. Greggg has a robust architecture and acts as a tour guide on-campus, both indoors or outdoors. This research allows one to build a customized robot at a low cost, under U.S. $2,000, for accomplishing the desired hospitality tasks, and scale, and expand the capability of the robot as required.

Practical Implications: The practical implication of the research is the capability to build and program a robot for hospitality tasks. Greggg is a customizable robot capable of giving on-campus tours both indoors and outdoors. In its current architecture, Greggg can be trained to be a museum docent and give directions to visitors on-campus or at an airport and scaled up for other hospitality tasks using off-the-shelf components. Enhancing the robot by scaling it up and expanding it, in addition to testing it with a range of increasingly more difficult tasks using machine learning algorithms, is highly beneficial to advancing research on the use of robots in the hospitality sector. Greggg can also be used for Robot-as-a-service (Rass) applications.

Societal Implications: The economic implication of Greggg is the ease and low cost with which one, with minimal technology know-how, can construct an autonomous hospitality industry robot. This chapter details the hardware and software needed to build a low cost scalable and customizable autonomous robot for the hospitality industry without having to pay an exorbitant price.

Research/Limitations/Implications: This research allows one to build their own customized hospitality robot under U.S. $2,000. Given the cost of building the robot, it has limitations on the hospitality tasks it can perform. It can navigate on flat surfaces, has limited vision and speech processing capabilities and has a battery life not exceeding an hour. Furthermore, it does not have any robotic manipulators or tactile processing capabilities.