Wireless and Catheter-Free Bladder Pressure and Volume Sensor.

Continuous monitoring of bladder activity during normal daily activities would improve clinical diagnostics and understanding of the mechanisms underlying bladder function, or help validate how differing neuromodulation strategies affect the bladder. This work describes a urological monitor of conscious activity (UroMOCA). The UroMOCA included a pressure sensor, urine impedance-sensing electrodes, and wireless battery recharge and data transmission circuitry. Components were assembled on a circuit board and encapsulated with an epoxy/silicone molded package that allowed Pt-Ir electrode feedthrough for urine contact. Packaged UroMOCAs measured 12 × 18 × 6 mm. UroMOCAs continuously transmitted data from all onboard sensors at 10 Hz at 30 cm range, and ran for up to 44 hours between wireless recharges. After in vitro calibration, implantations were performed in 11 animals. Animals carried the device for 28 days, enabling many observations of bladder behavior during natural, conscious behavior. In vivo testing confirmed the UroMOCA did not impact bladder function after a two-week healing period. Pressure data in vivo were highly correlated to a reference catheter used during an anesthetized follow-up. Static volume sensor data were less accurate, but demonstrated reliable detection of bladder volume decreases, and distinguished between voiding and non-voiding bladder events.

A Flexible Implant for Multi-Day Monitoring of Colon Segment Activity.

Monitoring of colon activity is currently limited to tethered systems like anorectal manometry. These systems have significant drawbacks, but fundamentally limit the observation time of colon activity, reducing the likelihood of detecting specific clinical events. While significant technological advancement has been directed to mobile sensor capsules, this work describes the development and feasibility of a stationary sensor for describing the coordinated activity between neighboring segments of the colon. Unlike wireless capsules, this device remains in position and measures propagating pressure waves and impedances between colon segments to describe activity and motility. This low-power, flexible, wireless sensor-the colon monitor to capture activity (ColoMOCA) was validated in situ and in vivo over seven days of implantation. The ColoMOCA diameter was similar to common endoscopes to allow for minimally invasive diagnostic placement. The ColoMOCA included two pressure sensors, and three impedance-sensing electrodes arranged to describe the differential pressures and motility between adjacent colon segments. To prevent damage after placement in the colon, the ColoMOCA was fabricated with a flexible polyimide circuit board and a silicone rubber housing. The resulting device was highly flexible and suitable for surgical attachment to the colon wall. In vivo testing performed in eleven animals demonstrated suitability of both short term (less than 3 hours) and 7-day implantations. Data collected wirelessly from animal experiments demonstrated the ColoMOCA described colon activity similarly to wired catheters and allowed untethered, conscious monitoring of organ behavior.

Hands to Hexapods, Wearable User Interface Design for Specifying Leg Placement for Legged Robots.

Specifying leg placement is a key element for legged robot control, however current methods for specifying individual leg motions with human-robot interfaces require mental concentration and the use of both arm muscles. In this paper, a new control interface is discussed to specify leg placement for hexapod robot by using finger motions. Two mapping methods are proposed and tested with lab staff, Joint Angle Mapping (JAM) and Tip Position Mapping (TPM). The TPM method was shown to be more efficient. Then a manual controlled gait based on TPM is compared with fixed gait and camera-based autonomous gait in a Webots simulation to test the obstacle avoidance performance on 2D terrain. Number of Contacts (NOC) for each gait are recorded during the tests. The results show that both the camera-based autonomous gait and the TPM are effective methods in adjusting step size to avoid obstacles. In high obstacle density environments, TPM reduces the number of contacts to 25% of the fixed gaits, which is even better than some of the autonomous gaits with longer step size. This shows that TPM has potential in environments and situations where autonomous footfall planning fails or is unavailable. In future work, this approach can be improved by combining with haptic feedback, additional degrees of freedom and artificial intelligence.