Eye Drop Adherence With an Eye Drop Bottle Cap Monitor.

PRCIS: An eye drop bottle cap monitor with audio and visual alarms measured eye drop adherence in 50 subjects with glaucoma. Baseline adherence rates were too high to test if the alarms could improve adherence. PURPOSE: To determine if an eye drop bottle cap monitor can measure and improve adherence. MATERIALS AND METHODS: The Devers Drop Device (D3, Universal Adherence LLC) was designed to measure eye drop adherence by detecting bottle cap removal and replacement, and it can provide text, visual and audio alerts when a medication is due. In Stage 1, we determined baseline adherence for 50 subjects using a nightly eye drop over a 25-day period. Subjects with less than 90% baseline adherence were eligible for Stage 2. In Stage 2, we randomized subjects to receive either no reminder or automated D3 alerts for their nightly eye drop over a subsequent 25-day period. We defined adherence as the proportion of drops administered within 3 hours of the subjects' scheduled dosing time. Subjects completed 3 questions regarding satisfaction with the device and willingness to pay. RESULTS: The D3 monitor remained attached to the eye drop bottle cap for the duration of the study and collected adherence data in all 50 patients. In Stage 1, the mean adherence rate was 90 ± 18% (range 32-100%). Forty (80%) subjects had an adherence rate greater than 90%. Adherence rates were too high in Stage 1 to adequately test the effects of reminders in Stage 2. Ninety-eight percent (49/50) and 96% (48/50) of subjects agreed "the device always stayed attached to the bottle cap" and "I was able to use the device to take the drops", respectively. Patients would pay $61±83 (range $0-400) for a similar device to improve adherence. CONCLUSIONS: The D3 can measure eye drop adherence. Research subjects reported high satisfaction and willingness to pay for an eye drop bottle cap monitor. Glaucoma patients have high adherence when they are being monitored, and future studies with research subjects screened for poor adherence may further determine the benefit of electronic monitoring of adherence with and without electronic reminders.

A 10 M Ω, 50 kHz-40 MHz Impedance Measurement Architecture for Source-Differential Flow Cytometry.

A low-power, impedance-based integrated circuit (IC) readout architecture is presented for cell analysis and cytometry applications. A three-electrode layout and source-differential excitation cancels baseline current prior to the sensor front-end, which enables the use of a high-gain readout circuit for the difference current. A lock-in architecture is employed with down-conversion and up-conversion in the feedback loop, enabling high closed-loop gain (up to 10 M Ω) and high bandwidth (up to 40 MHz). A hybrid-RC feedback network mitigates the SNR degradation seen over a wide operating frequency range when using purely capacitive feedback. The effect of phase shift on the closed-loop system gain and noise performance are analyzed in detail, along with optimization strategies, and the design includes fine-grained phase adjustment to minimize phase error. The impedance sensor was fabricated in a 0.18 µ m CMOS process and consumes 9.7 mW with an operating frequency from 50 kHz to 40 MHz and provides adjustable bandwidth. Measurements demonstrate that the impedance sensor achieves 6 pA [Formula: see text] input-referred noise over 200 Hz bandwidth at 0.5 MHz modulation frequency. Combined with a microfluidic flow cell, measured results using this source-differential measurement approach are presented using both monodisperse and polydisperse sample solutions and demonstrate single-cell resolution, detecting 3 µ m diameter particles in solution with 22 dB SNR.

Wireless Smartphone Control using Electromyography and Automated Gesture Recognition.

In this paper, a wearable, wireless system is demonstrated using electromyography (EMG) signals for realtime control of a smartphone device. The system allows gesturebased control of a smartphone or tablet computer without physical contact, direct line of sight, or significant movement. Additionally, automated gesture detection is shifted to the smartphone, eliminating the need for robust computing hardware. The electronic system and gesture prediction algorithm are described, and measured results are presented and for multiple users. The system demonstrates a maximum true positive detection rate of 92% for a trained user, using three distinct hand gestures. The EMG-based detection system serves as a proof-of-concept for providing wireless, gesture-based control of computer interfaces using low-cost consumer hardware.