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Companies struggle with identifying relevant sustainability aspects strategically, assessing alternative solutions quantitatively, and making trade-offs. This paper reports results from a prescriptive study with an aerospace company, and presents the Sustainability Criteria And product life-cycle Data Simulation (SCADS) approach. Based on strategic integration of sustainability indicators, this approach aims to enable visualisation and comparison of the sustainability implications of different concepts in early design phases of product development.

Sustainable design methods and tools abound, but their implementation in practice remains marginal. This article brings together results from previous literature reviews and analyses of sustainable design methods and tools, as well as input from design researchers and professional practitioners to identify the needs and gaps in the area. It results in a shared vision of how sustainable design methods and tools can be more tightly integrated into mainstream product design and development, as well as the current state of practice and research in relation to four central questions: What are the needs and values of industry regarding sustainable design? What improvements in sustainable design methods and tools would most drive industry forward? How should researchers move forwardwith developingmore useful sustainable designmethods and tools? Howcan sustainable design be more effectively integrated into industry? A roadmap for the international sustainable design research community is proposed with descriptions of short-, medium-, and long-term tasks for addressing each question. The purpose is to support collective progress and discussions on method and tool development and adoption, and to enablemore tangible success inmainstreaming sustainable design practices in industry. © 2020 by the authors. open access

The communication of sustainability values shared between product developers and customers is an important catalyst for effective collaboration that inspires sustainable consumption. Despite the many tools developed for assessing and communicating the product’s sustainability performance, customers are facing difficulties in understanding product sustainability information. The knowledge gaps remain underexplored about how product sustainability information is perceived and how this impacts customer purchasing behaviour. This paper outlines a new approach, driven by both backcasting and forecasting thinking, for understanding and modelling customer preferences for product sustainability information. We report findings from a case study of a large workplace furniture manufacturer. The study explored the potential of (i) identifying prioritised sustainability attributes using sustainability design space (SDS) and (ii) applying machine learning to model customer preferences.

Eco labelling is the practice of eco information provision that most directly addresses consumer behaviour. Nowadays consumers are facing difficulties in perceiving and understanding existing eco labels. In previous work we proposed the conceptual framework of eco information individualisation which tailors eco labels according to the specific needs of individual users using contextual technologies. This paper extends the conceptual framework by introducing a more structured way of considering the personal data and product data requirements, and reports the development of a design toolkit that aims to support designers in the designing of individualised eco information. A design workshop was carried out to introduce the concept to designers, and evaluate the usability and usefulness of the toolkit. Positive responses were received. The design outputs generated from the workshop were considered largely feasible and have the potential to be developed into digital prototypes. These indicate that it is possible for designers to learn to design eco information individualisation in a short time. This paper is a step towards a greater understanding of designing individualised eco information.

Self-report methods such as experience sampling provide an important means of understanding individual behaviors. The arrival of wearable camera technologies opens up research opportunities to provoke and explore in-depth contextual self-reflection on individual behaviors. Studies with wearable cameras have the potential to extend or/and complement existing experience sampling methods and provide greater insight into human behavior. However, the use of wearable cameras raises distinctive ethical challenges. This paper outlines a number of ethical challenges occasioned by the use of wearable cameras in research, both tractable and intractable.

Eco labeling is one of the most important measures to encourage sustainable consumer behavior. However problems are identified in the consumer's ability to perceive and understand eco labels. The concept of information individualization was suggested to engage consumers by tailoring eco information that appeals to their preferences. In this paper we describe the design of a prototype based on the conceptual framework of eco information individualization. Using enabling technologies such as smartphone with NFC function, the proposed device will embed personal visual data (i.e. landscape photo retrieved from the user's Facebook page) into an individualized eco label. It is believed that the photo would provoke the user into reflecting on their experience and increase their attention to eco labels. We suggest a framework that evaluates the effectiveness of the device, and discuss some related issues of qualitative and quantitative research approaches.

A MAG-muIMU which is based on MEMS accelerometers, gyroscopes and magnetometers has been developed for real-time estimation of human hand motions. Appropriate filtering, transformation and sensor fusion techniques are combined in the ubiquitous digital writing instrument (UDWI) to record the handwriting on any surface. However, because of the sensors' intrinsic biases and random noise such as circuit thermal noise, a calibration system that provides good reference measurement parameters must be used to compare the output of the MAG-muIMU sensors. We propose here a novel idea to calibrate three-dimensional linear accelerations, angular velocities and space attitude through optical tracking techniques. The optical tracking system (OTS) developed by our group consists of two parts: 1) 2D trajectory calibration that is used to obtain linear accelerations of the UDWI in a particular frame defined by us; 2) multiple camera calibration that is used for attitude calibration of the UDWI. An essential relationship to transform reference frames and angular velocities can be guaranteed after real-time attitude calibration. Hence, the entire nine-dimensional output of the MAG-muIMU can be rectified according to these more accurate data obtained from optical tracking.

A Micro Inertial Measurement unit (μIMU) which is based on Micro-Electro-Mechanical Systems (MEMS) accelerometers and gyroscope sensors is developed for real-time recognition of human hand motion. By using appropriate filtering, transformation and sensor fusion algorithms, a ubiquitous digital writing instrument is produced for recording handwriting on any surface. In this paper, we propose a method for deriving an error feedback to a Kalman filter based on the assumption that writing occurs only in two dimensions i.e. the writing surface is flat. By imposing this constraint, error feedback to the Kalman filter can be derived. Details of the feedback algorithm will be discussed and experimental results of its implementation are compared with the simple Kalman filter without feedback information.

A MAG-muIMU which is based on MEMS gyroscopes, accelerometers, and magnetometers is developed for real-time estimation of human hand motions. Appropriate filtering, transformation and sensor fusion techniques are combined in the ubiquitous digital writing instrument to record handwriting on any surface. In this paper, we discuss the design of an extended Kalman filter based on MAG-muIMU (micro inertial measurement unit with magnetometers) for real-time attitude tracking. The filter utilizes the gyroscope propagation for transient updates and correction by reference field sensors, such as gravity sensors, magnetometers or star trackers. A process model is derived to separate sensor bias and to minimize wideband noise. The attitude calculation is based on quaternion which, when compared to Euler angles, has no singularity problem. Testing with synthetic data and actual sensor data proved the filter will converge and accurately track the attitude of a rigid body. Our goal is to implement this algorithm for motion recognition of a 3D ubiquitous digital pen.