An improved extraction method for acrylamide determination in fruit and vegetable chips through enzyme addition.

An enzyme-addition method to pretreat fried fruit and vegetable chips for acrylamide analysis is reported, followed by determination of the acrylamide contents in 36 marketed fruit and vegetable chip products using LC-MS/MS. To improve the extraction process, the FDA method was modified. Specifically, digestive enzymes were added, overcoming the clogging of filters (or SPE cartridges) after extraction of vegetable chips using water. Diastase was added to extract high-starch products, including potato chips. Recoveries of 90.3-105.5% acrylamide were obtained at the spiking levels of 25-500 µg/kg. LOD and LOQ were similar between the method with (4.5 and 13.7 µg/kg) and without diastase addition (4.4 and 13.2 µg/kg). Okra chip with high mucin content was extracted after adding pepsin. This method provided a recovery of 99.8-102.2%, LOD of 6.0 µg/kg, and LOQ of 18.1 µg/kg. Both methods could be used for analyzing acrylamide, with critical method parameters satisfying European Union regulations.

AI Therapist Realizing Expert Verbal Cues for Effective Robot-Assisted Gait Training.

Repetitive and specific verbal cues by a therapist are essential in aiding a patient's motivation and improving the motor learning process. The verbal cues comprise various expressions, sentences, volumes, and timings, depending on the therapist's proficiency. This paper proposes an AI therapist (AI-T) that implements the verbal cues of professional therapists having extensive experience with robot-assisted gait training using the SUBAR for stroke patients. The AI-T was developed using a neuro-fuzzy system, a machine learning technique leveraging the benefits of fuzzy logic and artificial neural networks. The AI-T was trained with the professional therapist's verbal cue data, as well as clinical and robotic data collected from robot-assisted gait training with real stroke patients. Ten clinical data and 16 robotic data are input variables, and six verbal cues are output variables. Fifty-eight stroke patients wore the SUBAR, a gait training robot, and participated in the robot-assisted gait training. A total of 9059 verbal cue data, 580 clinical data of stroke patients, and 144 944 robotic data were collected from 693 training sessions. Test results show that the trained AI-T can implement six types of verbal cues with 93.7% accuracy for the 1812 verbal cue data of the professional therapist. Currently, the trained AI-T is deployed in the SUBAR and provides six verbal cues to stroke patients in robot-assisted gait training.

Research on Terrain Identification of the Smart Prosthetic Ankle by Fuzzy Logic.

This research suggests a fuzzy-logic based terrain identification method and the smart prosthetic ankle system, which automatically controls its ankle angle, based on the detected terrain environment, to assist comfortable gait performance of transtibial amputee. Suggested terrain identification method uses shank angle from three different stages of the stance phase in gait cycle (foot-flat, heel-strike, and toe-off) as input for the fuzzy-logic calculation, and detects five different terrain environment (flat, up-slope, down-slope, up-stairs, and down-stairs) within a single step of gait. Suggested smart prosthetic ankle system comprises of 1) load-cell to measure GRF (ground reaction force), 2) IMU (inertial measurement unit) sensor to measure shank angle, 3) actuator and four bar-linkage mechanism to control ankle angle accordingly for detected terrain environment, and 4) MCU (microcontroller unit) to carry out calculations and control algorithm for ankle actuation. To verify the accuracy of the terrain identification method of the system, the experiment was conducted, which consisted of four transtibial amputees to walk on five different terrain conditions, and the result has shown 97.5% detection accuracy. Compared to previous studies, our suggested smart prosthetic ankle system, along with its terrain identification algorithm, uses lesser number of sensors and step cycle to accurately detect gait environment, which may lead to providing better gait assistance and practical convenience for transtibial amputees.

A research on the postural stability of a person wearing the lower limb exoskeletal robot by the HAT model.

This paper proposes a method of detecting the postural stability of a person wearing the lower limb exoskeletal robot with the HAT(Head-Arm-Trunk) model. Previous studies have shown that the human posture is stable when the CoM(Center of Mass) of the human body is placed on the BoS(Base of Support). In the case of the lower limb exoskeletal robot, the motion data, which are used for the CoM estimation, are acquired by sensors in the robot. The upper body, however, does not have sensors in each segment so that it may cause the error of the CoM estimation. In this paper, the HAT(Head-Arm-Trunk) model which combines head, arms, and torso into a single segment is considered because the motion of head and arms are unknown due to the lack of sensors. To verify the feasibility of HAT model, the reflecting markers are attached to each segment of the whole human body and the exact motion data are acquired by the VICON to compare the COM of the full body model and HAT model. The difference between the CoM with full body and that with HAT model is within 20mm for the various motions of head and arms. Based on the HAT model, the XCoM(Extrapolated Center of Mass) which includes the velocity of the CoM is used for prediction of the postural stability. The experiment of making unstable posture shows that the XCoM of the whole body based on the HAT model is feasible to detect the instance of postural instability earlier than the CoM by 20-250 msec. This result may be used for the lower limb exoskeletal robot to prepare for any action to prevent the falling down.