Octopus: A Design Methodology for Motion Capture Wearables

Abstract

Human motion capture (MoCap) is widely recognised for its usefulness and application in different fields, such as health, sports, and leisure; therefore, its inclusion in current wearables (MoCap-wearables) is increasing, and it may be very useful in a context of intelligent objects interconnected with each other and to the cloud in the Internet of Things (IoT). However, capturing human movement adequately requires addressing difficult-to-satisfy requirements, which means that the applications that are possible with this technology are held back by a series of accessibility barriers, some technological and some regarding usability. To overcome these barriers and generate products with greater wearability that are more efficient and accessible, factors are compiled through a review of publications and market research. The result of this analysis is a design methodology called Octopus, which ranks these factors and schematises them. Octopus provides a tool that can help define design requirements for multidisciplinary teams, generating a common framework and offering a new method of communication between them.

Virtual Reality and Choreographic Practice: The Potential for New Creative Methods

“…Nowadays, it is possible to record or even real-time capture motion with affordable sensors and suits like the Notch sensors, the Rokoko Smartsuit Pro or the XSENS MVN Awinda (Marin, Blanco and Marin 2017). This is beginning to allow more dancers and choreographers to experiment with these technologies.…”

Human Actions Analysis: Templates Generation, Matching and Visualization Applied to Motion Capture of Highly-Skilled Karate Athletes

“…The tracking frequency was set to 100 Hz with 0.5 degrees static accuracy and 2 degrees dynamic accuracy. This motion capture system has already been used and cited in scientific research; for example, [ 42 , 43 ]. Data measured by inertial sensors of our costume are processed by the software provided by the hardware manufacturer to calculate the above-mentioned MoCap signal values according to the hierarchical model in Figure 1 .…”

Using the Internet of Things to Support Emotional Health

“…Collection support deals with how to support the user in terms of collecting data (dimensions [1][2][3][4], which includes what data to record, when and how often to do so, and assistance provided in the data collection. Procedural support is about how the app supports users in their daily use of the app such as by providing notifications that it is time to record data and by providing encouragement and other types of feedback (dimensions 4-5).…”

“…Similar to how wearable health tracking devices connected to the IOT can track physical metrics such as steps and heart rate for personal use or in a healthcare context [2] [3], data relating to emotions can also be tracked using wearable devices, mobile applications, smart objects or spaces, and other sensors as part of the IOT.…”

“…Nowadays, IOT has become more complex in terms of user experience [11], human computer interaction, and design process requirements [3] [1]. In this context emotion-tracking allows designers and developers to create products and services with better interfaces and more suited to the user's needs [12] [13].…”