Traditional walkers are commonly used for the elderly in social life, which solves the basic problem of walking, but it is difficult to ensure safety when a fall occurs, and the human-computer interaction is poor. The image recognition method or the IMU sensor method fixed on the user, such as a wearable watch, is used by most of the current fall detection methods. Wearable sensors require the user's wearing operation, which is a little troublesome, and the detection accuracy is related to the way of wearing. The image recognition method requires a high-priced camera and a fixed installation position, which is unable to adapt to outdoor activities. We investigated a low-cost method of mounting the sensor on the body of the walker. We propose in this paper an improved fall detection method, namely Precondition and Limit Threshold SPRT (PLT-SPRT), and a novel fall detection system on the smart walker based on PLT-SPRT. The signals of the upper and lower limb sensors are fused based on the Kalman filter algorithm, and the admittance control parameters are obtained through the system identification method. In this study, the improved sequential probability ratio test algorithm is used to set the null hypothesis and the alternative hypothesis, construct the likelihood ratio and optimize the decision function, which is used to judge whether falls occur. The system is simulated by Matlab software, the user intention after fusion is more accurate, and the optimized decision function is judged accurately. Verified by the embedded system based on STM32 of the smart walker equipment in the real world, it can accurately identify the fallen state, with low detection delay, and the fallen state is detected about 160ms earlier than the traditional threshold-based detection algorithm, at the same time, the accuracy is higher than 94.9%, which meets the high real-time requirements of fall detection and is the ideal solution for smart walkers.
Background
Regional citrate anticoagulation (RCA), a complex and effective technique, is recommended as the anticoagulation of choice for continuous renal replacement therapy. One of its key objectives is to keep the ionized calcium in the targeted range. In this study, we aimed to develop an automated RCA based on online monitoring of the ionized calcium concentration and closed‐loop feedback.
Methods
We constructed calcium‐selective electrodes with liquid inner contact, which measured a potentiometric signal as the output. We tested the responses, stability, and selectivity of the electrodes in flowing fluid containing calcium chloride. We compared the measurement accuracy between the electrodes and an i‐STAT system in vivo. Moreover, we established closed‐loop feedback using a proportional–integral–derivative controller model. We performed simulated automated RCA both in vivo and in vitro.
Results
The electrode gave a Nernstian response to the variation of ionized calcium concentration. It showed high stability and a relatively short response time. Changes in the fluid flow rate, solution PH, and addition of metal ions including Mg2+ and K+ did not interfere with the measurements of ionized calcium. These measurements in whole blood by the electrode were very close to those assessed by the i‐STAT system. The feedback control system responded quickly to an abnormal ionized calcium concentration and regulated the infusion rates of calcium or citrate to maintain the concentration of ionized calcium within the targeted range.
Conclusions
We successfully trialed automated RCA, which may help simplify the complexities of RCA in the future.
Aiming at the problem of object model identification of modern industrial process control systems, a new closed-loop moment parameter identification online method based on the data of normal operation of the running system is proposed. In this method, only one step response data of the system is required, and appropriate convergence factors are introduced into the Laplace formula, the trapezoidal integral method is used to calculate the values of two derivatives of the transfer function, then the four unknown parameters of the second-order model can be solved by fitting the data with the least square method, and the target model can be identified. Finally, the simulation results of building different objects through Matlab show that the identification method has general applicability and good robustness with high recognition, and it is not sensitive to noise signals.
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