A Multiple Stakeholder Perspective on the Drivers and Barriers for the Implementation of Lifestyle Monitoring using Infrared Sensors to Record Movements for Vulnerable Older Adults Living Alone at Home: A Qualitative Study

A variety of technologies classified as lifestyle monitoring (LM) allows, by unobtrusive monitoring, for supporting of living alone at home of vulnerable older adults, especially persons with neurocognitive disorders such as dementia. It can detect health deterioration, facilitate early intervention, and possibly help people avoid hospital admission. However, for LM to redeem its intended effects, it is important to be adopted by involved stakeholders such as informal and formal caregivers and care managers. Therefore, the aim of this qualitative study is to understand factors that drive or impede successful implementation of LM for vulnerable older adults, specifically using infrared sensors to record movements, studied from a multiple stakeholder perspective. An open coding process was used to identify key themes of the implementation process. Data were arranged according to a thematic framework based on the normalization process theory (NPT). All stakeholders agreed that LM could lead to various health benefits for older adults using LM. However, some did not perceive the LM system to be cost-efficient and expressed a need for more flexible health care structures for LM to be successfully implemented. All stakeholders acknowledged the fact that LM requires a transition of care and responsibilities, a clear eligibility strategy for clients, and a clear ambassador strategy for health care professionals, as well as reliable technology. This study highlights the complex nature of implementing LM and suggests the need for alignment within constructs of the NPT among stakeholders about new ways of collaboration in supporting living alone at home.

Bionic for Training: Smart Framework Design for Multisensor Mechatronic Platform Validation

Home monitoring supports the continuous improvement of the therapy by sharing data with healthcare professionals. It is required when life-threatening events can still occur after hospital discharge such as neonatal apnea. However, multiple sources of external noise could affect data quality and/or increase the misdetection rate. In this study, we developed a mechatronic platform for sensor characterizations and a framework to manage data in the context of neonatal apnea. The platform can simulate the movement of the abdomen in different plausible newborn positions by merging data acquired simultaneously from three-axis accelerometers and infrared sensors. We simulated nine apnea conditions combining three different linear displacements and body postures in the presence of self-generated external noise, showing how it is possible to reduce errors near to zero in phenomena detection. Finally, the development of a smart 8Ws-based software and a customizable mobile application were proposed to facilitate data management and interpretation, classifying the alerts to guarantee the correct information sharing without specialized skills.

An Automated Canine Line-Up for Detection Dog Research

Currently, there is a need to develop technology that facilitates and improves detection dog research. The aim of this research was to develop an automated computer-driven olfactory line-up task. The apparatus consisted of three olfactometers. Each olfactometer was equipped with flow meters to regulate air flow and dilution and six solenoid valves connected to odor jars. Each olfactometer generated an odor which was carried to an odor port where the dogs sample it. The olfactometer's valves were activated by a microcontroller, and a Python program was built to control each olfactometer and randomize and balance the odor presentation. Dogs (N = 12) received one or two 40-trial training sessions in a day where they progressed through a series of training phases where they learned to detect and alert to double-base smokeless powder (SP). An “alert” consisted of a 4-s nose hold. This was measured by infrared sensors in the ports. For each trial, the apparatus recorded dogs' search latency, sniff time, port entries, and response. All this information was automatically recorded in a csv file. A photoionization detector (PID) and solid-phase microextraction followed by gas chromatography-mass spectrometry (SPME-GC/MS) were used to evaluate the odor dynamics and to instrumentally verify odor presence and clearance. A control test was conducted at the end of the training to ensure dogs were alerting exclusively to the odorant. All 12 dogs readily learned to operate the apparatus within 23 days, and all exceeded 85% accuracy. Control tests indicated dogs were leveraging only olfactory cues and not unintentional cues such as auditory cues from the apparatus. Analytical data showed that odor was detected in the port immediately after the activation of a valve and that odor clearance occurred immediately after the valve was closed. The apparatus developed was easy to operate by the dogs and allowed substantial data collection using double-blind testing procedures in a very short period at an affordable cost point for research equipment (~$5,000 USD). The apparatus may prove to be a useful research tool to provide optimal odor stimuli control, ensure double-blind conditions, reduce labor, and significantly increase the amount of data collected.

Soft Array Surface-Changing Compound Eye

The field-of-view (FOV) of compound eyes is an important index for performance evaluation. Most artificial compound eyes are optical, fabricated by imitating insect compound eyes with a fixed FOV that is difficult to adjust over a wide range. The compound eye is of great significance in the field of tracking high-speed moving objects. However, the tracking ability of a compound eye is often limited by its own FOV size and the reaction speed of the rudder unit matched with the compound eye, so that the compound eye cannot better adapt to tracking high-speed moving objects. Inspired by the eyes of many organisms, we propose a soft-array, surface-changing compound eye (SASCE). Taking soft aerodynamic models (SAM) as the carrier and an infrared sensor as the load, the basic model of the variable structure infrared compound eye (VSICE) is established using an array of infrared sensors on the carrier. The VSICE model is driven by air pressure to change the array surface of the infrared sensor. Then, the spatial position of each sensor and its viewing area are changed and, finally, the FOV of the compound eye is changed. Simultaneously, to validate the theory, we measured the air pressure, spatial sensor position, and the FOV of the compound eye. When compared with the current compound eye, the proposed one has a wider adjustable FOV.

Acceptance and Preferences of Using Ambient Sensor-Based Lifelogging Technologies in Home Environments

Diverse sensor-based technologies can be used to track (older and frail) people’s movements and behaviors in order to detect anomalies and emergencies. Using several ambient sensors and integrating them into an assisting ambient system allows for the early identification of emergency situations and health-related changes. Typical examples are passive infrared sensors (PIR), humidity and temperature sensors (H&T) as well as magnetic sensors (MAG). So far, it is not known whether and to what extent these three specific sensor types are perceived and accepted differently by future users. Therefore, the present study analyzed the perception of benefits and barriers as well as acceptance of these specific sensor-based technologies using an online survey (reaching N=312 German participants). The results show technology-related differences, especially regarding the perception of benefits. Furthermore, the participants estimated the costs of these sensors to be higher than they are, but at the same time showed a relatively high willingness to pay for the implementation of sensor-based technologies in their home environment. The results enable the derivation of guidelines for both the technical development and the communication and information of assisting sensor-based technologies and systems.

Safety driven intelligent autonomous vehicle for smart cities using IoT

Purpose Autonomous vehicles rely on IoT-based technologies to take numerous decisions in real-time situations. However, added information from the sensor readings will burden the system and cause the sensors to produce inaccurate readings. To overcome these issues, this paper aims to focus on communication between sensors and autonomous vehicles for better decision-making in real-time. The system has unique features to detect the upcoming and ongoing vehicles automatically without intervention of humans in the system. It also predicts the type of vehicle and intimates the driver. Design/methodology/approach The system is designed using the ATmega 328 P and ESP 8266 chip. Information from ultrasonic and infrared sensors are analyzed and updated in the cloud server. The user can access all these real-time data at any point of time. The stored information in cloud servers is used for integrating artificial intelligence into the system. Findings The real-time sensor information is used to predict the surrounding environment and the system responds to the user according to the situation. Practical implications The system is implemented on embedded platform with IoT technology. The sensor information is updated to the cloud using the Blynk application for the user in real time. Originality/value The system is proposed for smart cities with IoT technology where the user and the system are aware of the surrounding environment. The system is mainly concerned with the accuracy of sensors and the distance between the vehicles in real-time environment.