Missing tags detection algorithm for radio frequency identification (RFID) data stream

RFID technology is a radio frequency identification services that provide a reader reading the information of items from the tags. Nowadays, RFID system is rapidly become more common in our live because it cheaper and smaller to be track, trace and identify the items. However, missing tag detection in RFID can occur due to RFID operating environment such as signal collisions and interferences. Missing tags also called as false negative reads is a tag that is present but it cannot be read by the nearby reader. The consequences of this problem can be enormous to business, as it will cause the system to report incorrect data due to an incorrect number of tags being detected. In fact, the performance of RFID missing tag detection is largely affected by uncertainty, which should be considered in the detecting process phase to minimize its negative impact. Thus in this research, an AC complement algorithm with hashing algorithm and Detect False Negative Read algorithm (DFR) is used to developed the Missing Tags Detection Algorithm (MTDA). AC complement algorithm was used to compare the different in each set of data. Meanwhile, DFR algorithm was used to identify the false negative read that present in the set of data. There are many approaches has been proposed to include Window Sub-range Transition Detection (WSTD), Efficient Missing-Tag Detection Protocol (EMD) and Multi-hashing based Missing Tag Identification (MMTI) protocol. This algorithm development has been guided by methodology in four stages. There stages including data preparation, simulation design, detecting false negative read strategy and performance measurement. MTDA can perform well in detecting false negative read with 100% detected in 3.25 second. This performance shows that the algorithm performs well in execution time in detecting false negative reads. In conclusion, it will give insight on the current challenges and open up to new solution to solve the problem of missing tag detection

Desenvolvimento de uma Abordagem para a Identificação e Localização de Pessoas em Ambientes Assistidos

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Sistemas de Informação.Pesquisadores têm mostrado em seus projetos que o envelhecimento da população é um fato já vivenciado por diversos países do mundo. Este fato faz com que exista a necessidade da criação de novas tecnologias e modelos para auxiliar no cuidado de pacientes em ambientes externos a hospitais, como em seus próprios domicílios, por exemplo. A existência de tal demanda impulsionou estudos na área de microprocessadores e de ambientes inteligentes fazendo com que diversas abordagens e tecnologias fossem desenvolvidas para auxiliar no cuidado de pacientes com o objetivo de aumentar a qualidade de vida dessas pessoas. Tendo em vista a necessidade de novas tecnologias e do aperfeiçoamento do cuidado de pacientes à distância, este trabalho de pesquisa propôs uma abordagem para localizar e identificar usuários dentro de um ambiente domiciliar através do uso da tecnologia Bluetooth. Para isto, foram estudados conceitos, estado da arte, tecnologias, modelos e padrões utilizados e projetado um modelo que implementa a abordagem apresentada. O modelo foi criado de modo a atingir o menor custo financeiro e energético possível, sendo adaptável a ambientes assistidos, que preservasse a identidade do usuário e que os dados obtidos fossem armazenados e disponibilizados, tudo da maneira menos invasiva possível. Após projetado e desenvolvido, o modelo foi testado em diferentes cenários, mostrando-se capaz de atingir os objetivos propostos pela pesquisa

Tag-free indoor fall detection using transformer network encoder and data fusion

This work presents a radio frequency identification (RFID)-based technique to detect falls in the elderly. The proposed RFID-based approach offers a practical and efficient alternative to wearables, which can be uncomfortable to wear and may negatively impact user experience. The system utilises strategically positioned passive ultra-high frequency (UHF) tag array, enabling unobtrusive monitoring of elderly individuals. This contactless solution queries battery-less tag and processes the received signal strength indicator (RSSI) and phase data. Leveraging the powerful data-fitting capabilities of a transformer model to take raw RSSI and phase data as input with minimal preprocessing, combined with data fusion, it significantly improves activity recognition and fall detection accuracy, achieving an average rate exceeding 96.5%. This performance surpasses existing methods such as convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM), demonstrating its reliability and potential for practical implementation. Additionally, the system maintains good accuracy beyond a 3-m range using minimal battery-less UHF tags and a single antenna, enhancing its practicality and cost-effectiveness

CrossSense:towards cross-site and large-scale WiFi sensing

We present CrossSense, a novel system for scaling up WiFi sensing to new environments and larger problems. To reduce the cost of sensing model training data collection, CrossSense employs machine learning to train, off-line, a roaming model that generates from one set of measurements synthetic training samples for each target environment. To scale up to a larger problem size, CrossSense adopts a mixture-of-experts approach where multiple specialized sensing models, or experts, are used to capture the mapping from diverse WiFi inputs to the desired outputs. The experts are trained offline and at runtime the appropriate expert for a given input is automatically chosen. We evaluate CrossSense by applying it to two representative WiFi sensing applications, gait identification and gesture recognition, in controlled single-link environments. We show that CrossSense boosts the accuracy of state-of-the-art WiFi sensing techniques from 20% to over 80% and 90% for gait identification and gesture recognition respectively, delivering consistently good performance – particularly when the problem size is significantly greater than that current approaches can effectively handle

Reconocimiento y Clasificación de Actividades Infantiles Utilizando Sonido Ambiental

En este trabajo se describe de manera detallada el contexto sobre el cual se desarrolla el presente trabajo a cerca del reconocimiento y clasificación de actividades infantiles utilizando sonido ambiental, como propuesta de tema para la tesis doctoral. A su vez, se presenta el planteamiento específico del problema, analizando los factores que influyen en él y las consideraciones a tomar en cuenta. Se describen además, de manera breve, las soluciones propuestas a través de este trabajo para abordar el problema aquí tratado, mencionando los métodos aplicados para llegar a ellas. También se muestra la hipótesis de investigación, así como el objetivo general y los objetivos específicos. En la parte final se presentan las contribuciones hechas con la realización del presente trabajo y la forma en la que está estructurado este documento

Kinematic assessment for stroke patients in a stroke game and a daily activity recognition and assessment system

Stroke is the leading cause of serious, long-term disabilities among which deficits in motor abilities in arms or legs are most common. Those who suffer a stroke can recover through effective rehabilitation which is delicately personalized. To achieve the best personalization, it is essential for clinicians to monitor patients' health status and recovery progress accurately and consistently. Traditionally, rehabilitation involves patients performing exercises in clinics where clinicians oversee the procedure and evaluate patients' recovery progress. Following the in-clinic visits, additional home practices are tailored and assigned to patients. The in-clinic visits are important to evaluate recovery progress. The information collected can then help clinicians customize home practices for stroke patients. However, as the number of in-clinic sessions is limited by insurance policies, the recovery information collected in-clinic is often insufficient. Meanwhile, the home practice programs report low adherence rates based on historic data. Given that clinicians rely on patients to self-report adherence, the actual adherence rate could be even lower. Despite the limited feedback clinicians could receive, the measurement method is subjective as well. In practice, classic clinical scales are mostly used for assessing the qualities of movements and the recovery status of patients. However, these clinical scales are evaluated subjectively with only moderate inter-rater and intra-rater reliabilities. Taken together, clinicians lack a method to get sufficient and accurate feedback from patients, which limits the extent to which clinicians can personalize treatment plans. This work aims to solve this problem. To help clinicians obtain abundant health information regarding patients' recovery in an objective approach, I've developed a novel kinematic assessment toolchain that consists of two parts. The first part is a tool to evaluate stroke patients' motions collected in a rehabilitation game setting. This kinematic assessment tool utilizes body-tracking in a rehabilitation game. Specifically, a set of upper body assessment measures were proposed and calculated for assessing the movements using skeletal joint data. Statistical analysis was applied to evaluate the quality of upper body motions using the assessment outcomes. Second, to classify and quantify home activities for stroke patients objectively and accurately, I've developed DARAS, a daily activity recognition and assessment system that evaluates daily motions in a home setting. DARAS consists of three main components: daily action logger, action recognition part, and assessment part. The logger is implemented with a Foresite system to record daily activities using depth and skeletal joint data. Daily activity data in a realistic environment were collected from sixteen post-stroke participants. The collection period for each participant lasts three months. An ensemble network for activity recognition and temporal localization was developed to detect and segment the clinically relevant actions from the recorded data. The ensemble network fuses the prediction outputs from customized 3D Convolutional-De-Convolutional, customized Region Convolutional 3D network and a proposed Region Hierarchical Co-occurrence network which learns rich spatial-temporal features from either depth data or joint data. The per-frame precision and the per-action precision were 0.819 and 0.838, respectively, on the validation set. For the recognized actions, the kinematic assessments were performed using the skeletal joint data, as well as the longitudinal assessments. The results showed that, compared with non-stroke participants, stroke participants had slower hand movements, were less active, and tended to perform fewer hand manipulation actions. The assessment outcomes from the proposed toolchain help clinicians to provide more personalized rehabilitation plans that benefit patients.Includes bibliographical references