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We present the FuwaFuwa sensor module, a round, handsize, wireless device for measuring the shape deformations of soft objects such as cushions and plush toys. It can be embedded in typical soft objects in the household without complex installation procedures and without spoiling the softness of the object because it requires no physical connection. Six LEDs in the module emit IR light in six orthogonal directions, and six corresponding photosensors measure the reflected light energy. One can easily convert almost any soft object into a touch-input device that can detect both touch position and surface displacement by embedding multiple FuwaFuwa sensor modules in the object. A variety of example applications illustrate the utility of the FuwaFuwa sensor module. An evaluation of the proposed deformation measurement technique confirms its effectiveness.ACM Classification: H5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous.

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Cross-sectional study. Objective. To develop a binary classification model for cervical myelopathy (CM) screening based on a machine learning algorithm using Leap Motion (Leap Motion, San Francisco, CA), a novel noncontact sensor device. Summary of Background Data. Progress of CM symptoms are gradual and cannot be easily identified by the patients themselves. Therefore, screening methods should be developed for patients of CM before deterioration of myelopathy. Although some studies have been conducted to objectively evaluate hand movements specific to myelopathy using cameras or wearable sensors, their methods are unsuitable for simple screening outside hospitals because of the difficulty in obtaining and installing their equipment and the long examination time. Methods. In total, 50 and 28 participants in the CM and control groups were recruited, respectively. The diagnosis of CM was made by spine surgeons. We developed a desktop system using Leap Motion that recorded 35 parameters of fingertip movements while participants gripped and released their fingers as rapidly as possible. A support vector machine was used to develop the binary classification model, and a multiple linear regression analysis was performed to create regression models to estimate the total Japanese Orthopaedic Association (JOA) score and the JOA score of the motor function of the upper extremity (MU-JOA score).Results. The binary classification model indexes were as follows: sensitivity, 84.0%; specificity, 60.7%; accuracy, 75.6%; area under the curve, 0.85. The Spearman rank correlation coefficient between the estimated score and the total JOA score was 0.44 and that between the estimated score and the MU-JOA score was 0.51. Conclusion. Our binary classification model using a machine learning algorithm and Leap Motion could classify CM with high sensitivity and would be useful for CM screening in daily life before consulting doctors and telemedicine.