Conventional strain sensors measure strains exerted on solid metals and have been widely applied. Stretch measurements of flexible objects require strain sensors with wide dynamic range (stretch exceeding 100%) that can also measure areal changes. Flexible strain sensors are expected to realize a wide range of technologies, such as human interfaces, smart clothes, skin-motion monitoring, and robotic skin. Recently, carbon nanotubes (CNTs) have been assembled into stretchable conductors, and are potential base materials for various flexible sensors. Herein, we construct a flexible stretching sensor from urethane elastomer and conductive electrodes from singlewalled CNTs. This sensor is extremely thin (thickness: 150 µm), and characterized by high elasticity (up to 100%), low stress (0.8 MPa at 100%), durability (1000 cycles at 50%), light weight (approx. 1.1 g/cm 3 ), and sensitivity (1 pF/mm 2 ). The strain sensor is tested on a cloth fabric, and is confirmed to measure the stretch area of flexible materials.Index Terms-Capacitive sensor, thin film sensor, strain sensor, carbon nanotubes.

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BackgroundBody movements, such as trunk flexion and rotation, are risk factors for low back pain in occupational settings, especially in healthcare workers. Wearable motion capture systems are potentially useful to monitor lower back movement in healthcare workers to help avoid the risk factors. In this study, we propose a novel system using sheet stretch sensors and investigate the system validity for estimating lower back movement.MethodsSix volunteers (female:male = 1:1, mean age: 24.8 ± 4.0 years, height 166.7 ± 5.6 cm, weight 56.3 ± 7.6 kg) participated in test protocols that involved executing seven types of movements. The movements were three uniaxial trunk movements (i.e., trunk flexion-extension, trunk side-bending, and trunk rotation) and four multiaxial trunk movements (i.e., flexion + rotation, flexion + side-bending, side-bending + rotation, and moving around the cranial–caudal axis). Each trial lasted for approximately 30 s. Four stretch sensors were attached to each participant’s lower back. The lumbar motion angles were estimated using simple linear regression analysis based on the stretch sensor outputs and compared with those obtained by the optical motion capture system.ResultsThe estimated lumbar motion angles showed a good correlation with the actual angles, with correlation values of r = 0.68 (SD = 0.35), r = 0.60 (SD = 0.19), and r = 0.72 (SD = 0.18) for the flexion-extension, side bending, and rotation movements, respectively (all P < 0.05). The estimation errors in all three directions were less than 3°.ConclusionThe stretch sensors mounted on the back provided reasonable estimates of the lumbar motion angles. The novel motion capture system provided three directional angles without capture space limits. The wearable system possessed great potential to monitor the lower back movement in healthcare workers and helping prevent low back pain.

Low-back pain is a common affliction. Epidemiological analyses have reported that periodic cycles of lumbar flexion and rotation are major risk factors for low-back pain. To prevent low-back pain, a lumbar-motion monitoring device could help diagnosticians assess patients’ risk for low-back pain. This study proposes such a device that uses lightweight stretchable strain sensors. Six of these strain sensors form a parallel-sensor mechanism that measures rotation angles of lumbar motion in three axes. The parallel-sensor mechanism calculates rotation angles from the lengths of the strain sensors iteratively. Experimental results reveal that the prototype device is effective for lumbar-motion measurement and significantly improves in terms of wearability over comparable devices.

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In the modern society with a variety of information electronic devices, human interfaces increase their importance in a boundary of a human and a device. In general, the human is required to get used to the device. Even if the device is designed as a universal device or a high-usability device, the device is not suitable for all users. The usability of the device depends on the individual user. Therefore, personalized and customized human interfaces are effective for the user. To create customized interfaces, we propose rapid prototyping human interfaces using stretchable strain sensors. The human interfaces comprise parts formed by a three-dimensional printer and the four strain sensors. The three-dimensional printer easily makes customized human interfaces. The outputs of the interface are calculated based on the sensor’s lengths. Experiments evaluate three human interfaces: a sheet-shaped interface, a sliding lever interface, and a tilting lever interface. We confirm that the three human interfaces obtain input operations with a high accuracy.