Culture-Free Quantification of Bacteria Using Digital Fluorescence Imaging in a Tunable Magnetic Capturing Cartridge for Onsite Food Testing.

Accurate, onsite detection of pathogenic bacteria from food matrices is required to rapidly respond to pathogen outbreaks. However, accurately detecting whole-cell bacteria in large sample volumes without an enrichment step remains a challenge. Therefore, bacterial samples must be concentrated, identified, and quantified. We developed a tunable magnetic capturing cartridge (TMCC) and combined it with a portable digital fluorescence reader for quick, onsite, quantitative detection of Staphylococcus aureus. The TMCC platform integrates an absorption pad impregnated with water-soluble polyvinyl alcohol (PVA) with an injection-molded polycarbonate (PC) plate that has a hard magnet on its back and an acrylonitrile-butadiene-styrene case. An S. aureus-specific antibody conjugated with magnetic nanoparticles was used to concentrate bacteria from a large-volume sample and capture bacteria within the TMCC. The retention time for capturing bacteria on the TMCC was adjusted by controlling the concentration and volume of the PVA solution. Concentrated bacterial samples bound to target-specific aptamer probes conjugated with quantum dots were loaded into the TMCC for a controlled time, followed by attachment of the bacteria to the PC plate and removal of unbound aptamer probes with wash buffer. The captured bacteria were quantified using a digital fluorescence reader equipped with an embedded program that automatically counts fluorescently tagged bacteria. The bacterial count made using the TMCC was comparable to a standard plate count (R2 = 0.9898), with assay sensitivity and specificity of 94.3 and 100%, respectively.

Techniques for Improving the Reliability of Prosthesis Wearer Muscle Signals Using Pressure and EMG Sensors.

In this study, we propose a technique to increase the reliability of muscle signals in case of weak muscle signal measurements using pressure sensors in a prosthesis wearer for measuring the degree of contraction of the muscle. This technique is applied when determining the intention of the wearer of an intelligent prosthesis. Through this technique, it is possible to determine the intention of a prosthesis wearer, which was previously not possible.

A study on the operation of rehabilitation interfaces in active rehabilitation exercises for upper limb hemiplegic patients: Interfaces for lateral and bilateral exercises.

BACKGROUND: For implementing autonomous rehabilitation exercises for upper limb hemiplegic patients, interfaces and a rehabilitation scenario that allow lateral and bilateral motions in a rehabilitation exercise robot are proposed. OBJECTIVE: The proposed method measures the motion information generated from the unaffected part and projects it to an affected part in which the affected part expresses motions of the unaffected part. METHODS: Both the accelerometer and gyro data were merged for estimating the motion information of the unaffected part. Also, HDR and complementary filters were applied to improve measurement errors in a data merging process. RESULTS: For verifying the proposed method, a device, which is similar to a human body joint, was fabricated. Then, the angular values estimated by using an inertial sensor and the encoder values from the device were compared. In addition, a camera analysis was used to verify the proposed rehabilitation scenario by applying the rehabilitation interface proposed in this study to an exo-skeleton robot arm. CONCLUSION: It is possible to apply the method proposed in this study to the control variables in different upper limb rehabilitation exercise robots. Thus, it is expected that patient centered active lateral/bilateral rehabilitation exercises can be performed through this interface method.

Hand region extraction and gesture recognition from video stream with complex background through entropy analysis.

Hand gesture recognition utilizing image processing relies upon recognition through markers or hand extraction by colors, and therefore is heavily restricted by the colors of clothes or skin. We propose a method to recognize band gestures extracted from images with a complex background for a more natural interface in HCI (human computer interaction). The proposed method obtains the image by subtracting one image from another sequential image, measures the entropy, separates hand region from images, tracks the hand region and recognizes hand gestures. Through entropy measurement, we have color information that has near distribution in complexion for regions that have big values and extracted hand region from input images. We could draw the hand region adaptively in variable lighting or individual differences because entropy offers color information as well as motion information at the same time. The detected contour using chain code for the hand region is extracted, and present centroidal profile method that is improved little more and recognized gesture of hand. In the experimental results for 6 kinds of hand gesture, it shows the recognition rate with more than 95% for person and 90-100% for each gesture at 5 frames/sec.