3DKnITS: Three-dimensional Digital Knitting of Intelligent Textile Sensor for Activity Recognition and Biomechanical Monitoring.

We present an approach to develop seamless and scalable piezo-resistive matrix-based intelligent textile using digital flat-bed and circular knitting machines. By combining and customizing functional and common yarns, we can design the aesthetics and architecture and engineer both the electrical and mechanical properties of a sensing textile. By incorporating a melting fiber, we propose a method to shape and personalize three-dimensional piezo-resistive fabric structure that can conform to the human body through thermoforming principles. It results in a robust textile structure and intimate interfacing, suppressing sensor drifts and maximizing accuracy while ensuring comfortability. This paper describes our textile design, fabrication approach, wireless hardware system, deep-learning enabled recognition methods, experimental results, and application scenarios. The digital knitting approach enables the fabrication of 2D to 3D pressure-sensitive textile interiors and wearables, including a 45 x 45 cm intelligent mat with 256 pressure-sensing pixels, and a circularly-knitted, form-fitted shoe with 96 sensing pixels across its 3D surface both with linear piezo-resistive sensitivity of 39.4 for up to 500 N load. Our personalized convolutional neural network models are able to classify 7 basic activities and exercises and 7 yoga poses in-real time with 99.6% and 98.7% accuracy respectively. Further, we demonstrate our technology for a variety of applications ranging from rehabilitation and sport science, to wearables and gaming interfaces.

A lab-on-a-chip for the concurrent electrochemical detection of SARS-CoV-2 RNA and anti-SARS-CoV-2 antibodies in saliva and plasma.

Rapid, accurate and frequent detection of the RNA of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) and of serological host antibodies to the virus would facilitate the determination of the immune status of individuals who have Coronavirus disease 2019 (COVID-19), were previously infected by the virus, or were vaccinated against the disease. Here we describe the development and application of a 3D-printed lab-on-a-chip that concurrently detects, via multiplexed electrochemical outputs and within 2 h, SARS-CoV-2 RNA in saliva as well as anti-SARS-CoV-2 immunoglobulins in saliva spiked with blood plasma. The device automatedly extracts, concentrates and amplifies SARS-CoV-2 RNA from unprocessed saliva, and integrates the Cas12a-based enzymatic detection of SARS-CoV-2 RNA via isothermal nucleic acid amplification with a sandwich-based enzyme-linked immunosorbent assay on electrodes functionalized with the Spike S1, nucleocapsid and receptor-binding-domain antigens of SARS-CoV-2. Inexpensive microfluidic electrochemical sensors for performing multiplexed diagnostics at the point of care may facilitate the widespread monitoring of COVID-19 infection and immunity.

Mechanical Imaging of Soft Tissues With Miniature Climbing Robots.

Systematically mapping the mechanical properties of skin and tissue is useful for biomechanics research and disease diagnostics. For example, later stage breast cancer and lymphoma manifest themselves as hard nodes under the skin. Currently, mechanical measurements are done manually, with a sense of touch or a handheld tool. Manual measurements do not provide quantitative information and vary depending on the skill of the practitioner. Research shows that tactile sensors could be more sensitive than a hand. We propose a method that uses our previously developed skin-crawling robots to noninvasively test the mechanical properties of soft tissue. Robots are more systematic and repeatable than humans. Using the data collected with a cutomoter or indenter integrated into the miniature robot, we trained a convolutional neural network to classify the size and depth of the lumps. The classification works with 98.8% accuracy for cutometer and 99.6% for indenter for lump size with a diameter of 0 to 10 mm embedded in depth of 1 to 5 mm in a simulated tissue. We conducted a limited evaluation on a forearm, where the robot imaged dry skin with a cutometer. We hope to improve the ability to test tissues noninvasively, and ultimately provide better sensitivity and systematic data collection.

A Wide-Range, Wireless Wearable Inertial Motion Sensing System for Capturing Fast Athletic Biomechanics in Overhead Pitching.

The standard technology used to capture motion for biomechanical analysis in sports has employed marker-based optical systems. While these systems are excellent at providing positional information, they suffer from a limited ability to accurately provide fundamental quantities such as velocity and acceleration (hence forces and torques) during high-speed motion typical of many sports. Conventional optical systems require considerable setup time, can exhibit sensitivity to extraneous light, and generally sample too slowly to accurately capture extreme bursts of athletic activity. In recent years, wireless wearable sensors have begun to penetrate devices used in sports performance assessment, offering potential solutions to these limitations. This article, after determining pressing problems in sports that such sensors could solve and surveying the state-of-the-art in wearable motion capture for sports, presents a wearable dual-range inertial and magnetic sensor platform that we developed to enable an end-to-end investigation of high-level, very wide dynamic-range biomechanical parameters. We tested our system on collegiate and elite baseball pitchers, and have derived and measured metrics to glean insight into performance-relevant motion. As this was, we believe, the first ultra-wide-range wireless multipoint and multimodal inertial and magnetic sensor array to be used on elite baseball pitchers, we trace its development, present some of our results, and discuss limitations in accuracy from factors such as soft-tissue artifacts encountered with extreme motion. In addition, we discuss new metric opportunities brought by our systems that may be relevant for the assessment of micro-trauma in baseball.

Real-time Mental State Recognition using a Wearable EEG.

The increasing quality and availability of low-cost EEG systems offer new possibilities for non-medical purposes. Existing openly available algorithms to assess the user's mental state in real-time have been mainly performed with medical-grade equipment. In this paper, an approach to assess the user's Focus or Relax states in real-time using a consumer-grade, wearable EEG headband is evaluated. One naive measure and four entropy-based measures, computed using relative frequency band powers in the EEG signal, were introduced. Classifiers for relax and focus state detection, based on the estimation of probability distributions, were developed and evaluated in a user study. Results showed that the Tsallis entropy-based measure performed best for the Focus score, whereas the Renyi measure performed best for the Relax score. Sensitivities of 82.0% and 80.4% with specificities of 82.8% and 80.8% were achieved for the Focus and Relax scores, respectively. The results demonstrated the possibilities of using a wearable EEG system for real-time mental state recognition.

Gait analysis using a shoe-integrated wireless sensor system.

We describe a wireless wearable system that was developed to provide quantitative gait analysis outside the confines of the traditional motion laboratory. The sensor suite includes three orthogonal accelerometers, three orthogonal gyroscopes, four force sensors, two bidirectional bend sensors, two dynamic pressure sensors, as well as electric field height sensors. The "GaitShoe" was built to be worn in any shoe, without interfering with gait and was designed to collect data unobtrusively, in any environment, and over long periods. The calibrated sensor outputs were analyzed and validated with results obtained simultaneously from the Massachusetts General Hospital, Biomotion Laboratory. The GaitShoe proved highly capable of detecting heel-strike and toe-off, as well as estimating foot orientation and position, inter alia.