In this paper a stain sensor to measure large strain (80%) in textiles is presented. It consists of a mixture of 50wt-% thermoplastic elastomer (TPE) and 50wt-% carbon black particles and is fiber-shaped with a diameter of 0.315mm. The attachment of the sensor to the textile is realized using a silicone film. This sensor configuration was characterized using a strain tester and measuring the resistance (extension-retraction cycles): It showed a linear resistance response to strain, a small hysteresis, no ageing effects and a small dependance on the strain velocity. The total mean error caused by all these effects was +/-5.5% in strain. Washing several times in a conventional washing machine did not influence the sensor properties. The paper finishes by showing an example application where 21 strain sensors were integrated into a catsuit. With this garment, 27 upper body postures could be recognized with an accuracy of 97%.

In this paper we present a garment prototype using strain sensors to recognize upper body postures. A novel thermoplastic elastomer strain sensor was used for measuring strain in the clothing. This sensor has a linear resistance response to strain, a small hysteresis and can be fully integrated into textile. A study was conducted with eight participants wearing the garment and performing a total of 27 upper body postures. A Naive Bayes classification was applied to identify the different postures. Nearly a complete recognition rate of 97% was achieved when the classification was adapted to the individual participant. A classification rate of 84% was achieved for an all-user classification and 65% for an independent user. These results show the feasibility to recognize postures with our setup, even in an unseen user setting. Furthermore, we used the garment prototype in a gym experiment to explore its potential for rehabilitation and fitness training. Intensity, speed and number of repetitions could be obtained from the garment sensor data.

In this paper we introduce the concept of a wearable assistant for swimmer, called SwimMaster. The SwimMaster consists of acceleration sensors with micro-controllers and feedback interface modules that swimmer wear while swimming. With four different evaluation studies and a total of 22 subjects we demonstrate the functionality and power of the SwimMaster system. We show how a wide range of swim parameters can be monitored and used for a continuous swim performance evaluation. These parameters include the time per lane, the swimming velocity and the number of strokes per lane. Also swim style specific factors like the body balance and the body rotation are extracted. Finally three feedback modalities are tested and evaluated. With these means we show the ability of the SwimMaster to assist a swimmer in achieving the desired exercise goals by constantly monitoring his/her swim performance and providing the necessary feedback to achieve the desired workout goals.

Design and characterization of a novel soft and stretchable inertial sensor based on a eutectic gallium–indium droplet and silicone elastomer.

This paper presents a pure textile, capacitive pressure sensor designed for integration into clothing to measure pressure on human body. The applications fields cover all domains where a soft and bendable sensor with a high local resolution is needed, e.g. in rehabilitation, pressure-sore prevention or motion detection due to muscle activities. We developed several textile sensors with spatial resolution of 2 times 2 cm and an average error below 4 percent within the measurement range 0 to 10 N/cm². Applied on the upper arm the textile pressure sensor determines the deflection of the forearm between 0 and 135 degrees due to the muscle bending.

City-scale mass gatherings attract hundreds of thousands of pedestrians. These pedestrians need to be monitored constantly to detect critical crowd situations at an early stage and to mitigate the risk that situations evolve towards dangerous incidents. Hereby, the crowd density is an important characteristic to assess the criticality of crowd situations. In this work, we consider location-aware smartphones for monitoring crowds during mass gatherings as an alternative to established video-based solutions. We follow a participatory sensing approach in which pedestrians share their locations on a voluntary basis. As participation is voluntarily, we can assume that only a fraction of all pedestrians shares location information. This raises a challenge when concluding about the crowd density. We present a methodology to infer the crowd density even if only a limited set of pedestrians share their locations. Our methodology is based on the assumption that the walking speed of pedestrians depends on the crowd density. By modeling this behavior, we can infer a crowd density estimation. We evaluate our methodology with a real-world data set collected during the Lord Mayor’s Show 2011 in London. This festival attracts around half a million spectators and we obtained the locations of 828 pedestrians. With this data set, we first verify that the walking speed of pedestrians depends on the crowd density. In particular, we identify a crowd density-dependent upper limit speed with which pedestrians move through urban spaces. We then evaluate the accuracy of our methodology by comparing our crowd density estimates to ground truth information obtained from video cameras used by the authorities. We achieve an average calibration error of and confirm the appropriateness of our model. With a discussion of the limitations of our methodology, we identify the area of application and conclude that smartphones are a promising tool for crowd monitoring.