A new, continuously-monitoring portable device that monitors the diabetic foot has shown to help in reduction of diabetic foot complications. Persons affected by diabetic foot have shown to be particularly sensitive in the plantar surface; this sensitivity coupled with certain ambient conditions may cause dry skin. This dry skin leads to the formation of fissures that may eventually result in a foot ulceration and subsequent hospitalization. This new device monitors the micro-climate temperature and humidity areas between the insole and sole of the footwear. The monitoring system consists of an array of ten sensors that take readings of relative humidity within the range of 100% ± 2% and temperature within the range of −40 °C to 123.8 ± 0.3 °C. Continuous data is collected using embedded C software and the recorded data is processed in Matlab. This allows for the display of data; the implementation of the iterative Gauss-Newton algorithm method was used to display an exponential response curve. Therefore, the aim of our system is to obtain feedback data and provide the critical information to various footwear manufacturers. The footwear manufactures will utilize this critical information to design and manufacture diabetic footwear that reduce the risk of ulcers in diabetic feet.

This article shows a method for the statistical analysis of signals. Firstly, this method was applied to analyze the processing of signs generated by an acquisition card for pulse measurement using the synchronous demodulation method. The application of the method allowed the study of each signal consisting of a descriptive statistical analysis, followed by the analysis of the trend and dynamics of the movement using the augmented Dickey–Fuller test and Hurst exponent, respectively. Secondarily, the method presented here supported the comparison between the pulse signals obtained by synchronous demodulation and plethysmography methods. In addition, the residuals from the pulse comparison of both methods were analyzed. To quantify the differences between the signals, these were compared using the mean-squared error, the root-mean-square error, the mean absolute error, the mean error, the mean absolute percentage error, and the mean percentage error. After this research, it was possible to analyze the signals knowing characteristics such as the following: the presence of normal, exponential, lognormal, and uniform distributions, stationary trend, and dynamic movement anti-persistent. The novelty that this article proposes is the use of concepts traditionally used in the study of time series and models of demand administration, now focused on supporting improvements over the different stages of design and conceptualization of signal processing devices.

Objective Giant lipomas are benign tumors larger than 5 cm in size that are very uncommun in the hand, with the extant literature limited to case reports and small case series. The aim of the present study is to describe our experience with giant lipomas at the level of the hand, reviewing the most important aspects in relation to their diagnosis and treatment. Material and Methods We present 6 patients treated in our service with giant lipomas of the hand between 2007 and 2015. Four cases only presented difficulty in grasping and mobilizing the hand due to the large size of the lipoma. Two cases were accompanied by a clinical feature of compression of the median nerve in relation to its location within the carpal tunnel. Results All patients underwent surgery, and a complete excision of the lipoma was performed. The functional results have been satisfactory in all cases. Conclusions Giant lipomas of the hand are infrequent tumors of slow growth, generally asymptomatic, although they can cause a compressive pathology due to the great size that they reach. Magnetic resonance imaging is an especially useful test to locate and accurately determine the size of the lesion in view of its surgical excision. After surgery, it is important to make a differential diagnosis with low-grade liposarcomas through an anatomopathological study, since both, macroscopically, have similar characteristics.

Achieving the highest levels of repeatability and precision, especially in robot manipulators applied in automation manufacturing, is a practical pose-recognition problem in robotics. Deviations from nominal robot geometry could produce substantial errors at the end effector, which can be more than 0.5 inches for a 6 ft robot arm. In this research, a pose-recognition system is developed for estimating the position of each robot joint and end-effector pose using image processing. To generate the joint angle, the system is developed via the modeling of a pose obtained by combining a convolutional neural network (CNN) and a multi-layer perceptron network (MLP). The CNN categorizes the input image generated by a remote monocular camera and generates a classification probability vector. The MLP generates a multiple linear regression model based on the probability vector generated by a CNN and describes the values of each joint angle. The proposed model is compared with the P-n-Perspective problem-solving method, which is based on marker tracking using ArUco markers and the encoder values. The system was verified using a robot manipulator with four degrees of freedom. Additionally, the proposed method exhibits superior performance in terms of joint-by-joint error, with an absolute error that is three units less than that of the computer vision method. Furthermore, when evaluating the end-effector pose, the proposed method showed a lower average standard deviation of 9mm compared with the computer vision method, which had a standard deviation of 13 mm.

The abalone, a gastropod mollusk belonging to the Haliotidae family, has been a subject of research with a focus on its behavior in response to various environmental factors, including temperature fluctuations and hypoxia. While existing studies have primarily explored abalone behavior in relation to environmental changes, there remains a significant amount to be discovered regarding their behavioral patterns. There are studies in which recordings of readings have been made (indirect methods) of the heart rate of the abalone since it is believed that it is one of the parameters affected due to the changes in temperature and dissolved oxygen in its environment. The main disadvantage of some procedures to measure heart rate in abalone is that an incision is made near the heart, through this incision the measurement is made using the photoplethysmography technique. Another parameter that is affected by these changes (temperature and hypoxia) is respiratory rate. Temperature is an important factor that can reduce oxygen solubility and thus alter abalone respiration; so in this work, we propose to use the technique of photoplethysmography: a method involving the measurement of changes in volume within a biological organ, which offers a unique opportunity to monitor these changes. We adapt this technique to measure gill movements in abalone, a critical indicator of respiratory activity in these marine mollusks. By utilizing a pulse oximetry setup and synchronous detection, we establish an innovative approach to gill movement analysis. This approach employs a pulse train and synchronous demodulation technique, the train of pulses is guided through an optical fiber to one of the respiratory orifices of the abalone to effectively detect and analyze the gill movements of abalone. Additionally, the role of frequency of its gill movements is highlighted as another critical variable of interest within the broader study of Haliotis fulgens, shedding light on the multifaceted nature of abalone behavior and responses to their surroundings. This research contributes to a deeper understanding of abalone physiology and behavior, offering insights into their adaptation to changing environmental conditions.

Photoplethysmography is a widely studied noninvasive optical technique with great potential for application in clinical medicine. There is evidence that the PPG signal can provide information about heart rate, oxygen saturation in arterial blood, respiratory rate among other cardiovascular signs. The two-wavelength photoplethysmographic signal is generally used to determine oxygen saturation in arterial blood under the principle that oxygenated and deoxygenated hemoglobin differentially absorb red and near-infrared wavelengths. In this work it is proposed to use the same wavelengths that are used in the pulse oximetry technique (660 nm and 940 nm) to calculate heart rate; Heart rate recovery is done by analyzing the data using the synchronous demodulation technique. The sampled data was generated from measurement carried out on two people. The heart rate obtained with the pulse oximetry technique in conjunction with the synchronous demodulation technique was corroborated with the heart rate obtained using the PPG signal at a single wavelength (940 nm).