Automatic Identification of Inertial Sensors on the Human Body Segments

In the last few years, inertial sensors (accelerometers and gyroscopes) in combination with magnetic sensors was proven to be a suitable ambulatory alternative to traditional human motion tracking systems based on optical position measurements. While accurate full 6 degrees of freedom information is available [1], these inertial sensor systems still have some drawbacks, e.g. each sensor has to be attached to a certain predefined body segment. The goal of this project is to develop a ‘Click-On-and-Play’ ambulatory 3D human motion capture system, i.e. a set of (wireless) inertial sensors which can be placed on the human body at arbitrary positions, because they will be identified and localized automatically

Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs

We address the problem of making human motion capture in the wild more practical by using a small set of inertial sensors attached to the body. Since the problem is heavily under-constrained, previous methods either use a large number of sensors, which is intrusive, or they require additional video input. We take a different approach and constrain the problem by: (i) making use of a realistic statistical body model that includes anthropometric constraints and (ii) using a joint optimization framework to fit the model to orientation and acceleration measurements over multiple frames. The resulting tracker Sparse Inertial Poser (SIP) enables 3D human pose estimation using only 6 sensors (attached to the wrists, lower legs, back and head) and works for arbitrary human motions. Experiments on the recently released TNT15 dataset show that, using the same number of sensors, SIP achieves higher accuracy than the dataset baseline without using any video data. We further demonstrate the effectiveness of SIP on newly recorded challenging motions in outdoor scenarios such as climbing or jumping over a wall.Comment: 12 pages, Accepted at Eurographics 201

No Grice: Computers that Lie, Deceive and Conceal

In the future our daily life interactions with other people, with computers, robots and smart environments will be recorded and interpreted by computers or embedded intelligence in environments, furniture, robots, displays, and wearables. These sensors record our activities, our behavior, and our interactions. Fusion of such information and reasoning about such information makes it possible, using computational models of human behavior and activities, to provide context- and person-aware interpretations of human behavior and activities, including determination of attitudes, moods, and emotions. Sensors include cameras, microphones, eye trackers, position and proximity sensors, tactile or smell sensors, et cetera. Sensors can be embedded in an environment, but they can also move around, for example, if they are part of a mobile social robot or if they are part of devices we carry around or are embedded in our clothes or body. \ud \ud Our daily life behavior and daily life interactions are recorded and interpreted. How can we use such environments and how can such environments use us? Do we always want to cooperate with these environments; do these environments always want to cooperate with us? In this paper we argue that there are many reasons that users or rather human partners of these environments do want to keep information about their intentions and their emotions hidden from these smart environments. On the other hand, their artificial interaction partner may have similar reasons to not give away all information they have or to treat their human partner as an opponent rather than someone that has to be supported by smart technology.\ud \ud This will be elaborated in this paper. We will survey examples of human-computer interactions where there is not necessarily a goal to be explicit about intentions and feelings. In subsequent sections we will look at (1) the computer as a conversational partner, (2) the computer as a butler or diary companion, (3) the computer as a teacher or a trainer, acting in a virtual training environment (a serious game), (4) sports applications (that are not necessarily different from serious game or education environments), and games and entertainment applications

BIOTEX-biosensing textiles for personalised healthcare management.

Textile-based sensors offer an unobtrusive method of continually monitoring physiological parameters during daily activities. Chemical analysis of body fluids, noninvasively, is a novel and exciting area of personalized wearable healthcare systems. BIOTEX was an EU-funded project that aimed to develop textile sensors to measure physiological parameters and the chemical composition of body fluids, with a particular interest in sweat. A wearable sensing system has been developed that integrates a textile-based fluid handling system for sample collection and transport with a number of sensors including sodium, conductivity, and pH sensors. Sensors for sweat rate, ECG, respiration, and blood oxygenation were also developed. For the first time, it has been possible to monitor a number of physiological parameters together with sweat composition in real time. This has been carried out via a network of wearable sensors distributed around the body of a subject user. This has huge implications for the field of sports and human performance and opens a whole new field of research in the clinical setting