HINNet + HeadSLAM: robust inertial navigation with machine learning for long-term stable tracking

In recent years, human position tracking with wearable sensors has been rapidly developed and shown great potential for applications within healthcare, smart homes, sports, and emergency services. Unlike tracking researches with sensors on the foot, human positioning studies with head-mounted sensors are fewer and still remain problems that have not been solved. We have proposed two studies solve part of the problems separately: HINNet is able to track people with free head rotations; HeadSLAM allows long-term tracking with stable errors. In this paper, to allow free head rotations meanwhile support long-term tracking, HINNet is combined with HeadSLAM and tested. The result shows that the combination could effectively distinguish head rotations and keep a low and stable position error in long-term tracking, with an absolute trajectory error (ATE) of 2.69m and relative trajectory error (RTE) of 3.52m

Non-contact infrared thermometers and thermal scanners for human body temperature monitoring: a systematic review

In recent years, non-contact infrared thermometers (NCITs) and infrared thermography (IRT) have gained prominence as convenient, non-invasive tools for human body temperature measurement. Despite their widespread adoption in a range of settings, there remain questions about their accuracy under varying conditions. This systematic review sought to critically evaluate the performance of NCITs and IRT in body temperature monitoring, synthesizing evidence from a total of 72 unique settings from 32 studies. The studies incorporated in our review ranged from climate-controlled room investigations to clinical applications. Our primary findings showed that NCITs and IRT can provide accurate and reliable body temperature measurements in specific settings and conditions. We revealed that while both NCITs and IRT displayed a consistent positive correlation with conventional, contact-based temperature measurement tools, NCITs demonstrated slightly superior accuracy over IRT. A total of 29 of 50 settings from NCIT studies and 4 of 22 settings from IRT studies achieved accuracy levels within a range of ±0.3 °C. Furthermore, we found that several factors influenced the performance of these devices. These included the measurement location, the type of sensor, the reference and tool, individual physiological attributes, and the surrounding environmental conditions. Our research underscores the critical need for further studies in this area to refine our understanding of these influential factors and to develop standardized guidelines for the use of NCITs and IRT

More than red tape: exploring complexity in medical device regulatory affairs

Introduction: This study investigates the complexity of regulatory affairs in the medical device industry, a critical factor influencing market access and patient care. Methods: Through qualitative research, we sought expert insights to understand the factors contributing to this complexity. The study involved semi-structured interviews with 28 professionals from medical device companies, specializing in various aspects of regulatory affairs. These interviews were analyzed using a mix of qualitative coding and natural language processing (NLP) techniques. Results: The findings reveal key sources of complexity within the regulatory landscape, divided into five domains: (1) regulatory language complexity, (2) intricacies within the regulatory process, (3) global-level complexities, (4) database-related considerations, and (5) product-level issues. Discussion: The participants highlighted the need for strategies to streamline regulatory compliance, enhance interactions between regulatory bodies and industry players, and develop adaptable frameworks for rapid technological advancements. Emphasizing interdisciplinary collaboration and increased transparency, the study concludes that these elements are vital for establishing coherent and effective regulatory procedures in the medical device sector

Uncovering Regulatory Affairs Complexity in Medical Products: A Qualitative Assessment Utilizing Open Coding and Natural Language Processing (NLP)

This study investigates the complexity of regulatory affairs in the medical device industry, a critical factor influencing market access and patient care. Through qualitative research, we sought expert insights to understand the factors contributing to this complexity. The study involved semi-structured interviews with 28 professionals from medical device companies, specializing in various aspects of regulatory affairs. These interviews were analyzed using open coding and Natural Language Processing (NLP) techniques. The findings reveal key sources of complexity within the regulatory landscape, divided into five domains: (A) Regulatory language complexity, (B) Intricacies within the regulatory process, (C) Global-level complexities, (D) Database-related considerations, and (E) Product-level issues. The participants highlighted the need for strategies to streamline regulatory compliance, enhance interactions between regulatory bodies and industry players, and develop adaptable frameworks for rapid technological advancements. Emphasizing interdisciplinary collaboration and increased transparency, the study concludes that these elements are vital for establishing coherent and effective regulatory procedures in the medical device sector

A modular, deep learning-based holistic intent sensing system tested with Parkinson’s disease patients and controls

People living with mobility-limiting conditions such as Parkinson’s disease can struggle to physically complete intended tasks. Intent-sensing technology can measure and even predict these intended tasks, such that assistive technology could help a user to safely complete them. In prior research, algorithmic systems have been proposed, developed and tested for measuring user intent through a Probabilistic Sensor Network, allowing multiple sensors to be dynamically combined in a modular fashion. A time-segmented deep-learning system has also been presented to predict intent continuously. This study combines these principles, and so proposes, develops and tests a novel algorithm for multi-modal intent sensing, combining measurements from IMU sensors with those from a microphone and interpreting the outputs using time-segmented deep learning. It is tested on a new data set consisting of a mix of non-disabled control volunteers and participants with Parkinson’s disease, and used to classify three activities of daily living as quickly and accurately as possible. Results showed intent could be determined with an accuracy of 97.4% within 0.5 s of inception of the idea to act, which subsequently improved monotonically to a maximum of 99.9918% over the course of the activity. This evidence supports the conclusion that intent sensing is viable as a potential input for assistive medical devices

Comparison of median frequency between traditional and functional sensor placements during activity monitoring

Long-term monitoring is of great clinical relevance. Accelerometers are often used to provide information about activities of daily living. The median frequency (f[subscript m]) of acceleration has recently been suggested as a powerful parameter for activity recognition. However, compliance issues arise when people need to integrate activity recognition sensors into their daily lives. More functional placements should provide higher levels of conformity, but may also affect the quality and generalizability of the signals. How f[subscript m] changes as a result of a more functional sensor placement remains unclear. This study investigates the agreement in f[subscript m] for a sensor placed on the back with one in the pocket across a range of daily activities. The translational and gravitational accelerations are also computed to determine if the accelerometer should be fused with additional sensors to improve agreement. Twelve subjects were tested over four tasks and only the “vertical” x-axis showed a moderate agreement (Intraclass Correlation Coefficient of 0.54) after correction for outliers. Generalizability across traditional and functional sensor locations might therefore be limited. Differentiation of the signal into a translational and gravitational component decreased the level of agreement further, suggesting that combined information streams are more robust to changing locations then singular data streams. Integrating multiple sensor modalities to obtain specific components is unlikely to improve agreement across sensor locations. More research is needed to explore measurement signals of more user friendly sensor configurations that will lead to a greater clinical acceptance of body worn sensor systems

On the protectiveness of additively manufactured mouthguards

A mouthguard is a piece of equipment worn in sports worldwide to greatly reduce the chance of orodental injuries. This work uses a newly developed method for testing and analysing mouthguards subjected to high impact energies of up to 100 J. This method allows investigation and comparison between the use of additive manufacturing, and current best mouthguard manufacturing technology of thermoforming with Ethylene-vinyl acetate (EVA). The impact experiments are conducted using a drop tower and high-speed images are captured for the further analysis. Important physical parameters such as peak force, impulse and dissipated energy in the mouthguard are determined. The results revealed that the additively manufactured mouthguards made from Arnitel® ID 2045 lead to a peak force and impulse of impact that is on average 10% and 25% lower, respectively, than that experienced when using the EVA mouthguard. A lower peak force and impulse is preferred as it reduces the chance of an orodental injury occurring. The research shows that previously mouthguards have been tested at impact energies far lower than those experienced in impact prone sports such as field hockey. When using impacts with higher energies, the findings show that additive manufacturing provides a viable technology for manufacturing mouthguards, which offers many new benefits