Sub-1.4 cm3 capsule for detecting labile inflammatory biomarkers in situ.

Transient molecules in the gastrointestinal tract such as nitric oxide and hydrogen sulfide are key signals and mediators of inflammation. Owing to their highly reactive nature and extremely short lifetime in the body, these molecules are difficult to detect. Here we develop a miniaturized device that integrates genetically engineered probiotic biosensors with a custom-designed photodetector and readout chip to track these molecules in the gastrointestinal tract. Leveraging the molecular specificity of living sensors1, we genetically encoded bacteria to respond to inflammation-associated molecules by producing luminescence. Low-power electronic readout circuits2 integrated into the device convert the light emitted by the encapsulated bacteria to a wireless signal. We demonstrate in vivo biosensor monitoring in the gastrointestinal tract of small and large animal models and the integration of all components into a sub-1.4 cm3 form factor that is compatible with ingestion and capable of supporting wireless communication. With this device, diseases such as inflammatory bowel disease could be diagnosed earlier than is currently possible, and disease progression could be more accurately tracked. The wireless detection of short-lived, disease-associated molecules with our device could also support timely communication between patients and caregivers, as well as remote personalized care.

Modular Optoelectronic System for Wireless, Programmable Neuromodulation During Free Behavior.

This work presents a modular, light-weight head-borne neuromodulation platform that achieves low-power wireless neuromodulation and allows real-time programmability of the stimulation parameters such as the frequency, duty cycle, and intensity. This platform is comprised of two parts: the main device and the optional intensity module. The main device is functional independently, however, the intensity control module can be introduced on demand. The stimulation is achieved through the use of energy-efficient µLEDs directly integrated in the custom-drawn fiber-based probes. Our platform can control up to 4 devices simultaneously and each device can control multiple LEDs in a given subject. Our hardware uses off-the-shelf components and has a plug and play structure, which allows for fast turn-over time and eliminates the need for complex surgeries. The rechargeable, battery-powered wireless platform uses Bluetooth Low Energy (BLE) and is capable of providing stable power and communication regardless of orientation. This presents a potential advantage over the battery-free, fully implantable systems that rely on wireless power transfer, which is typically direction-dependent, requires sophisticated implantation surgeries, and demands complex custom-built experimental apparatuses. Although the battery life is limited to several hours, this is sufficient to complete the majority of behavioral neuroscience experiments. Our platform consumes an average power of 0.5 mW, has a battery life of 12 hours.

EMG-based Real Time Facial Gesture Recognition for Stress Monitoring.

An electromyogram (EMG) signal acquisition system capable of real time classification of several facial gestures is presented. The training data consist of the facial EMG collected from 10 individuals (5 female/5 male). A custom-designed sensor interface integrated circuit (IC) consisting of an amplifier and an ADC, implemented in 65nm CMOS technology, has been used for signal acquisition [1]. It consumes 3.8nW power from a 0.3V battery. Feature extraction and classification is performed in software every 300ms to give real-time feedback to the user. Discrete wavelet transforms (DWT) are used for feature extraction in the time-frequency domain. The dimensionality of the feature vector is reduced by selecting specific wavelet decomposition levels without compromising the accuracy, which reduces the computation cost of feature extraction in embedded implementations. A support vector machine (SVM) is used for the classification. Overall, the system is capable of identifying several jaw movements such as clenching, opening the jaw and resting in real-time from a single channel EMG data, which makes the system suitable for providing biofeedback during sleeping and awake states for stress monitoring, bruxism, and several orthodontic applications such as temporomandibular joint disorder (TMJD).