Measuring and interpretation of brain wave signals through electroencephalography (EEG) is an emerging technology. The technique is traditionally applied in a clinical setting with EEG caps and conductive gels to ensure proper contact through a subject’s hair, and anticipate inter-subject anthropometric variations. Development of dry electrodes offers the potential to develop wearable EEG headsets. Such devices could induce medical and commercial applications. In this paper, we evaluate a prototype EEG headset that actively places electrodes at standardized positions on the subject’s head, where each electrode is applied with equal pressure. The system is designed for use with dry electrodes. Our research delivers a better understanding on the link between general level of comfort and possible useful clear data signals, that can be used in brain computer interfaces (BCI). The present study is confined to the impact of adjustable electrodes pressure on level of user comfort only. Levels of discomfort are assessed in twelve participants, wearing an EEG headset with controllable electrode pressure exerted at 14 locations. Of-the-shelf dry electrodes are used. In a first session, evenly distributed pressure is increased and afterwards decreased in fixed time intervals, going from 10 kPa to 30 kPa and vice versa with steps of 2 kPa. In a second session, a subject specific acceptable pressure level is retrieved from the data of the first session and constantly applied for 30 min. During this intervention, level of discomfort is assessed in a VAS-scale. Additional observation and surveys yields insights on user experience in wearing a pressure exerting EEG headset.

Parametric 3D human body models are valuable tools for ergonomic product design and statistical shape modelling (SSM) is a powerful technique to build realistic body models from a database of 3D scans. Like the underlying 3D scans, body models built from SSMs are typically represented with triangle meshes. Unfortunately, triangle meshes are not well supported by CAD software where spline geometry dominates. Therefore, we propose a methodology to convert databases of pre-corresponded triangle meshes into multi-patch B-spline SSMs. An evaluation on four 3D scan databases shows that our method is able to generate accurate and water-tight models while preserving inter-subject correspondences by construction. In addition, we demonstrate that such SSMs can be used to generate design manikins which can be readily used in SolidWorks for designing well conforming product parts.

The human body is a complex biomechanical system that exhibits many variations. Wearable
products should be both functional and comfortable. They require a close and accurate fit to the
body of the end-user. Current approaches to design body near products rely on 1D anthropometry
and unrealistic manikins, e.g. constructed from simple surfaces such as spheres and cylinders
connected by splines. With the uprising of 3D scanning, a myriad of accurate 3D body models
becomes available. In this paper we present a framework to use this 3D shape information in the
development of wearable products. The key concept that we introduce to achieve this extension, is
an enriched shape model: a statistical shape model of the human body that also contains all 1D
anthropometric data in it. With enriched shape models, a 3D shape can be parameterized with a
given set of anthropometric features. Thus the dense geometric information of an individual’s shape
can be obtained simply by tuning that individual’s anthropometric values. By designing on the
generated 3D surface, a product can be obtained that closely fits the individual’s shape. We thus
extend the method of linking 1D anthropometric data with the dimensions of a product. This results
in three design strategies that link both body shape with product geometry: design for collective fit,
design for fit within clusters and design for individual fit. Each strategy is explained and studied
with the design of wearable EEG headsets that fits the human head.