Fall Detection Using Channel State Information from WiFi Devices

Falls among the independently living elderly population are a major public health worry, leading to injuries, loss of confidence to live independently and even to death. Each year, one in three people aged 65 and older falls and one in five of them suffers fatal or non fatal injuries. Therefore, detecting a fall early and alerting caregivers can potentially save lives and increase the standard of living. Existing solutions, e.g. push-button, wearables, cameras, radar, pressure and vibration sensors, have limited public adoption either due to the requirement for wearing the device at all times or installing specialized and expensive infrastructure. In this thesis, a device-free, low cost indoor fall detection system using commodity WiFi devices is presented. The system uses physical layer Channel State Information (CSI) to detect falls. Commercial WiFi hardware is cheap and ubiquitous and CSI provides a wealth of information which helps in maintaining good fall detection accuracy even in challenging environments. The goals of the research in this thesis are the design, implementation and experimentation of a device-free fall detection system using CSI extracted from commercial WiFi devices. To achieve these objectives, the following contributions are made herein. A novel time domain human presence detection scheme is developed as a precursor to detecting falls. As the next contribution, a novel fall detection system is designed and developed. Finally, two main enhancements to the fall detection system are proposed to improve the resilience to changes in operating environment. Experiments were performed to validate system performance in diverse environments. It can be argued that through collection of real world CSI traces, understanding the behavior of CSI during human motion, the development of a signal processing tool-set to facilitate the recognition of falls and validation of the system using real world experiments significantly advances the state of the art by providing a more robust fall detection scheme

Behaviour Profiling using Wearable Sensors for Pervasive Healthcare

In recent years, sensor technology has advanced in terms of hardware sophistication and miniaturisation. This has led to the incorporation of unobtrusive, low-power sensors into networks centred on human participants, called Body Sensor Networks. Amongst the most important applications of these networks is their use in healthcare and healthy living. The technology has the possibility of decreasing burden on the healthcare systems by providing care at home, enabling early detection of symptoms, monitoring recovery remotely, and avoiding serious chronic illnesses by promoting healthy living through objective feedback. In this thesis, machine learning and data mining techniques are developed to estimate medically relevant parameters from a participant‘s activity and behaviour parameters, derived from simple, body-worn sensors. The first abstraction from raw sensor data is the recognition and analysis of activity. Machine learning analysis is applied to a study of activity profiling to detect impaired limb and torso mobility. One of the advances in this thesis to activity recognition research is in the application of machine learning to the analysis of 'transitional activities': transient activity that occurs as people change their activity. A framework is proposed for the detection and analysis of transitional activities. To demonstrate the utility of transition analysis, we apply the algorithms to a study of participants undergoing and recovering from surgery. We demonstrate that it is possible to see meaningful changes in the transitional activity as the participants recover. Assuming long-term monitoring, we expect a large historical database of activity to quickly accumulate. We develop algorithms to mine temporal associations to activity patterns. This gives an outline of the user‘s routine. Methods for visual and quantitative analysis of routine using this summary data structure are proposed and validated. The activity and routine mining methodologies developed for specialised sensors are adapted to a smartphone application, enabling large-scale use. Validation of the algorithms is performed using datasets collected in laboratory settings, and free living scenarios. Finally, future research directions and potential improvements to the techniques developed in this thesis are outlined

Recent Advances in Motion Analysis

The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application

Multimodal radar sensing for ambient assisted living

Data acquired from health and behavioural monitoring of daily life activities can be exploited to provide real-time medical and nursing service with affordable cost and higher efficiency. A variety of sensing technologies for this purpose have been developed and presented in the literature, for instance, wearable IMU (Inertial Measurement Unit) to measure acceleration and angular speed of the person, cameras to record the images or video sequence, PIR (Pyroelectric infrared) sensor to detect the presence of the person based on Pyroelectric Effect, and radar to estimate distance and radial velocity of the person. Each sensing technology has pros and cons, and may not be optimal for the tasks. It is possible to leverage the strength of all these sensors through information fusion in a multimodal fashion. The fusion can take place at three different levels, namely, i) signal level where commensurate data are combined, ii) feature level where feature vectors of different sensors are concatenated and iii) decision level where confidence level or prediction label of classifiers are used to generate a new output. For each level, there are different fusion algorithms, the key challenge here is mainly on choosing the best existing fusion algorithm and developing novel fusion algorithms that more suitable for the current application. The fundamental contribution of this thesis is therefore exploring possible information fusion between radar, primarily FMCW (Frequency Modulated Continuous Wave) radar, and wearable IMU, between distributed radar sensors, and between UWB impulse radar and pressure sensor array. The objective is to sense and classify daily activities patterns, gait styles and micro-gestures as well as producing early warnings of high-risk events such as falls. Initially, only “snapshot” activities (single activity within a short X-s measurement) have been collected and analysed for verifying the accuracy improvement due to information fusion. Then continuous activities (activities that are performed one after another with random duration and transitions) have been collected to simulate the real-world case scenario. To overcome the drawbacks of conventional sliding-window approach on continuous data, a Bi-LSTM (Bidirectional Long Short-Term Memory) network is proposed to identify the transitions of daily activities. Meanwhile, a hybrid fusion framework is presented to exploit the power of soft and hard fusion. Moreover, a trilateration-based signal level fusion method has been successfully applied on the range information of three UWB (Ultra-wideband) impulse radar and the results show comparable performance as using micro-Doppler signature, at the price of much less computation loads. For classifying ‘snapshot’ activities, fusion between radar and wearable shows approximately 12% accuracy improvement compared to using radar only, whereas for classifying continuous activities and gaits, our proposed hybrid fusion and trilateration-based signal level improves roughly 6.8% (before 89%, after 95.8%) and 7.3% (before 85.4%, after 92.7%), respectively

AI Modeling Approaches for Detecting, Characterizing, and Predicting Brief Daily Behaviors such as Toothbrushing using Wrist Trackers.

Continuous advancements in wrist-worn sensors have opened up exciting possibilities for real-time monitoring of individuals\u27 daily behaviors, with the aim of promoting healthier, more organized, and efficient lives. Understanding the duration of specific daily behaviors has become of interest to individuals seeking to optimize their lifestyles. However, there is still a research gap when it comes to monitoring short-duration behaviors that have a significant impact on health using wrist-worn inertial sensors in natural environments. These behaviors often involve repetitive micro-events that last only a few seconds or even microseconds, making their detection and analysis challenging. Furthermore, these micro-events are often surrounded by non-repetitive boundary events, further complicating the identification process. Effective detection and timely intervention during these short-duration behaviors are crucial for designing personalized interventions that can positively impact individuals\u27 lifestyles. To address these challenges, this dissertation introduces three models: mORAL, mTeeth, and Brushing Prompt. These models leverage wrist-worn inertial sensors to accurately infer short-duration behaviors, identify repetitive micro-behaviors, and provide timely interventions related to oral hygiene. The dissertation\u27s contributions extend beyond the development of these models. Firstly, precise and detailed labels for each brief and micro-repetitive behavior are acquired to train and validate the models effectively. This involved meticulous marking of the exact start and end times of each event, including any intervening pauses, at a second-level granularity. A comprehensive scientific research study was conducted to collect such data from participants in their free-living natural environments. Secondly, a solution is proposed to address the issue of sensor placement variability. Given the different positions of the sensor within a wristband and variations in wristband placement on the wrist, the model needs to determine the relative configuration of the inertial sensor accurately. Accurately determining the relative positioning of the inertial sensor with respect to the wrist is crucial for the model to determine the orientation of the hand. Additionally, time synchronization errors between sensor data and associated video, despite both being collected on the same smartphone, are addressed through the development of an algorithm that tightly synchronizes the two data sources without relying on an explicit anchor event. Furthermore, an event-based approach is introduced to identify candidate segments of data for applying machine learning models, outperforming the traditional fixed window-based approach. These candidate segments enable reliable detection of brief daily behaviors in a computationally efficient manner suitable for real-time. The dissertation also presents a computationally lightweight method for identifying anchor events using wrist-worn inertial sensors. Anchor events play a vital role in assigning unambiguous labels in a fixed-length window-based approach to data segmentation and effectively demarcating transitions between micro-repetitive events. Significant features are extracted, and explainable machine learning models are developed to ensure reliable detection of brief daily and micro-repetitive behaviors. Lastly, the dissertation addresses the crucial factor of the opportune moment for intervention during brief daily behaviors using wrist-worn inertial sensors. By leveraging these sensors, users can receive timely and personalized interventions to enhance their performance and improve their lifestyles. Overall, this dissertation makes substantial contributions to the field of real-time monitoring of short-duration behaviors. It tackles various technical challenges, provides innovative solutions, and demonstrates the potential for wrist-worn sensors to facilitate effective interventions and promote healthier behaviors. By advancing our understanding of these behaviors and optimizing intervention strategies, this research has the potential to significantly impact individuals\u27 well-being and contribute to the development of personalized health solutions

Distributed Computing and Monitoring Technologies for Older Patients

This book summarizes various approaches for the automatic detection of health threats to older patients at home living alone. The text begins by briefly describing those who would most benefit from healthcare supervision. The book then summarizes possible scenarios for monitoring an older patient at home, deriving the common functional requirements for monitoring technology. Next, the work identifies the state of the art of technological monitoring approaches that are practically applicable to geriatric patients. A survey is presented on a range of such interdisciplinary fields as smart homes, telemonitoring, ambient intelligence, ambient assisted living, gerontechnology, and aging-in-place technology. The book discusses relevant experimental studies, highlighting the application of sensor fusion, signal processing and machine learning techniques. Finally, the text discusses future challenges, offering a number of suggestions for further research directions

Methodology for detecting movements of interest in elderly people

RESUMEN: El aumento en la expectativa de vida, tanto en Colombia como a nivel mundial, requiere un mayor uso de tecnologías dentro del área de la salud que permita a los adultos mayores conservar su independencia y mejorar su calidad de vida. En esta tesis se analiza la problemática de caídas en adultos mayores independientes, cuyas consecuencias pueden minimizarse mediante un sistema portable de detección automática que envíe una alarma de forma oportuna. Como punto de partida se elaboró una base de datos con 38 participantes que realizaron 19 actividades de la vida diaria y simularon 15 tipos de caídas. Para ello se utilizó un dispositivo portable con un acelerómetro triaxial. Pruebas preliminares con algoritmos de extracción de características comúnmente usados en la literatura para discriminar entre caídas y actividades de la vida diaria presentaron una precisión de hasta 96%. Para ello se utilizó un clasificador de bajo costo computacional basado en umbral que pudiese funcionar en tiempo real en sistemas embebidos. Un análisis individual de actividades con cada uno de los algoritmos de extracción de características demostró que algunas de ellas son complementarias entre sí, este análisis se usó como punto de partida para desarrollar métricas no lineales que mejoraron la discriminación a un 99%. También se observó que muchos de los falsos positivos son debidos a actividades periódicas de alta aceleración, que pudieron ser detectados a partir de su periodo. Con el fin de garantizar que la metodología desarrollada fuese implementable en sistemas embebidos sin que ello signifique una alta carga computacional (y el consecuente consumo de batería), en este trabajo se propone un algoritmo basado en un filtro de Kalman, un pre procesamiento basado en un filtro Butterworth de cuarto orden, una métrica no lineal basada en dos características de extracción comúnmente usadas, y un clasificador basado en umbral. Este algoritmo fue implementado en un dispostivo embebido y validado mediante la simulación de las mismas actividades de la base de datos adquirida en este trabajo, además de una prueba piloto en condiciones reales con adultos mayores. Ambas pruebas presentaron una tasa de error inferior al 1%.ABSTRACT: The increase in life expectancy, both in Colombia and globally, requires higher use of healthcare technology to allow elderly adults maintain their independence and improve their quality of life. In this thesis, we analyze the problem of falls in independent elderly people. The consequences of a fall can be minimized by a portable automatic detection system, wich sends an alarm right after an event. We started by creating a dataset with 38 participants that conducted 19 activities of daily life and simulated 15 types of falls. They used a portable device with a triaxial accelerometer. Preliminary tests with feature extraction algorithms commonly used in the literature to discriminate between falls and activities of daily living presented up to 96% of accuracy. They were implemented with a low computational cost threshold-based classifier, which can operate in real-time on embedded systems. An individual activity analysis with each feature extraction algorithm demonstrated that some of them are complementary to each other. This analysis was used as a starting point to develop nonlinear discrimination metrics that improved the accuracy to 99%. We also noted that most false positives are due to high acceleration periodic activities, and we could detect them solely based on their period. In order to guarantee that the developed methodology can be implemented on embedded systems without affecting their computational capability (and the consequent battery consumption), we propose an algorithm based on a Kalman filter, with a pre-processing stage based on a 4-th order Butterworth filter, a non-linear feature based in two commonly used feature extraction characteristics, and a threshold-based classifier. This algorithm was implemented in an embedded device and validated by simulating the same activities of the dataset acquired in this work, along with a pilot test in real conditions with elderly adults. Both tests presented an error rate below 1%