Design and fabrication of a capacitance based wearable pressure sensor using e-textiles

This paper addresses the methods used for the design and fabrication of a capacitance based wearable pressure sensor fabricated using neoprene and (SAC) plated Nylon Fabric. The experimental set up for the pressure sensor is comprised of a shielded grid of sensing modules, a 555 timer based transduction circuitry, and an Arduino board measuring the frequency of signal to a corresponding pressure. The fundamental design parameters addressed during the development of the pressure sensor presented in this paper are based on size, simplicity, cost, adaptability, and scalability. The design approach adopted in this paper results in a sensor module that is less obtrusive, has a thinner and flexible profile, and its sensitivity is easily scalable for ‘smart’ product applications across industries associated to sports performance, ergonomics, rehabilitation, etc.

New artificial intelligence based tire size identification for fast and safe inflating cycle

Motor vehicle accidents are one of the main killers on the road. Modern vehicles have several safety features to improve the stability and controllability. The tire condition is critical to the proper function of the designed safety features. Under or over inflated tires adversely affects the stability of vehicles. It is generally the vehicle's user responsibility to ensure the tire inflation pressure is set and maintained to the required value using a tire inflator. In the tire inflator operation, the vehicle's user sets the desired value and the machine has to complete the task. During the inflation process, the pressure sensor does not read instantaneous static pressure to ensure the target value is reached. Hence, the inflator is designed to stop repetitively for pressure reading and avoid over inflation. This makes the inflation process slow, especially for large tires. This paper presents a novel approach using artificial neural network based technique to identify the tire size. Once the tire size is correctly identified, an optimized inflation cycle can be computed to improve performance, speed and accuracy of the inflation process. The developed neural network model was successfully simulated and tested for predicting tire size from the given sets of input parameters. The test results are analyzed and discussed in this paper.

Pressure injury: an exploration of the relationship between risk factors and interface pressure

Pressure injuries are a serious risk for patients admitted to hospital and are thought to result from a number of forces operating on skin tissue (pressure, shear and friction). Most research on interface pressure (IP) has taken place using healthy volunteers or mannequins. Little is currently known about the relationship between pressure injury risk and IP for hospital patients. This relationship was investigated with a sample of 121 adult hospital patients. Pressure injury risk was evaluated using the Waterlow Risk Assessment Tool (WRAT) and IP was measured at the sacrum using a Tekscan ClinSeatTM IP sensor mat. Other factors considered were body mass index (BMI), blood pressure, reason for hospital admission, comorbidities and admission route to hospital. Patients were classified according to WRAT categories (‘low risk’, ‘at risk’, ‘high risk’, ‘very high risk’) and then remained still on a standard hospital mattress for 10 minutes while IP was measured. Participants in the ‘low risk’ group were significantly younger than all other groups (p<0.001) and there were some group differences in BMI. IP readings were compared between the ‘low risk’ group and all of the participants at greater risk. The ‘low risk’ group had significantly lower IP at the sacrum on a standard hospital mattress than those at greater risk (p=0.002). Those at greater risk tended to have IP readings at the low end of the compromised IP range. This study is significant because it describes a new, clinically relevant methodology and presents findings that challenge clinician assumptions about the relationships between pressure injury risk assessment and IP.

Data monitoring sensor network for bignet research testbed

Equipped with recent advances in electronics and communication, wireless sensor networks gained a rapid development to provide reliable information with higher Quality of Service (QoS) at lower costs. This paper presents a realtime tracking system developed as a part of the ISSNIP BigNet Testbed project. Here a GPS receiver was used to acquire position information of mobile nodes and GSM technology was used as the data communication media. Moreover, Google map based data visualization software was developed to locate the mobile nodes via Internet. This system can be used to accommodate various sensors, such as temperature, pressure, pH etc., and monitor the status of the nodes.

Fabrication of force sensor circuits on wearable conductive textiles

This paper discusses design and fabrication processes in the development of a wearable and flexible conductive resistive sensor. The design and development of the sensor involve the use of Sn-Ag-Cu (SAC)plated Nylon fabric, precisionfused deposition modeling(FDM) using silicone and petrolatum for etch-resistant masks using the EnvisionTEC GmbH Bioplotter, and wet etching using Chromium, Ammonium Persulphate, and Salt-Vinegar etching solutions. Preliminary testing with other mask types, development processes, and sensor design approaches for various applications are discussed.

Online mining abnormal period patterns from multiple medical sensor data streams

With the advanced technology of medical devices and sensors, an abundance of medical data streams are available. However, data analysis techniques are very limited, especially for processing massive multiple physiological streams that may only be understood by medical experts. The state-of-the-art techniques only allow multiple medical devices to independently monitor different physiological parameters for the patient's status, thus they signal too many false alarms, creating unnecessary noise, especially in the Intensive Care Unit (ICU). An effective solution which has been recently studied is to integrate information from multiple physiologic parameters to reduce alarms. But it is a challenge to detect abnormalities from high frequently changed physiological streams data, since abnormalities occur gradually due to the complex situation of patients. An analysis of ICU physiological data streams shows that many vital physiological parameters are changed periodically (such as heart rate, arterial pressure, and respiratory impedance) and thus abnormalities are generally abnormal period patterns. In this paper, we develop a Mining Abnormal Period Patterns from Multiple Physiological Streams (MAPPMPS) method to detect and rank abnormalities in medical sensor streams. The efficiency and effectiveness of the MAPPMPS method is demonstrated by a real-world massive database of multiple physiological streams sampled in ICU, comprising 250 patients' streams (each stream involving over 1.3 million data points) with a total size of 28 GB data.

Following targets for mobile tracking in wireless sensor networks

Traditional tracking solutions in wireless sensor networks based on fixed sensors have several critical problems. First, due to the mobility of targets, a lot of sensors have to keep being active to track targets in all potential directions, which causes excessive energy consumption. Second, when there are holes in the deployment area, targets may fail to be detected when moving into holes. Third, when targets stay at certain positions for a long time, sensors surrounding them have to suffer heavier work pressure than do others, which leads to a bottleneck for the entire network. To solve these problems, a few mobile sensors are introduced to follow targets directly for tracking because the energy capacity of mobile sensors is less constrained and they can detect targets closely with high tracking quality. Based on a realistic detection model, a solution of scheduling mobile sensors and fixed sensors for target tracking is proposed. Moreover, the movement path of mobile sensors has a provable performance bound compared to the optimal solution. Results of extensive simulations show that mobile sensors can improve tracking quality even if holes exist in the area and can reduce energy consumption of sensors effectively.