Capturing accelerometer outputs in healthy volunteers under normal and simulated-pathological conditions using ML classifiers

Wearable devices offer a possible solution for acquiring objective measurements of physical activity. Most current algorithms are derived using data from healthy volunteers. It is unclear whether such algorithms are suitable in specific clinical scenarios, such as when an individual has altered gait. We hypothesized that algorithms trained on healthy population will result in less accurate results when tested in individuals with altered gait. We further hypothesized that algorithms trained on simulated-pathological gait would prove better at classifying abnormal activity.We studied healthy volunteers to assess whether activity classification accuracy differed for those with healthy and simulated-pathological conditions. Healthy participants (n=30) were recruited from the University of Leeds to perform nine predefined activities under healthy and simulated-pathological conditions. Activities were captured using a wrist-worn MOX accelerometer (Maastricht Instruments, NL). Data were analyzed based on the Activity-Recognition-Chain process. We trained a Neural-Network, Random-Forests, k-Nearest-Neighbors (k-NN), Support-Vector-Machines (SVM) and Naive Bayes models to classify activity. Algorithms were trained four times; once with 'healthy' data, and once with 'simulated-pathological data' for each of activity-type and activity-task classification. In activity-type instances, the SVM provided the best results; the accuracy was 98.4% when the algorithm was trained and then tested with unseen data from the same group of healthy individuals. Accuracy dropped to 52.8% when tested on simulated-pathological data. When the model was retrained with simulated-pathological data, prediction accuracy for the corresponding test set was 96.7%. Algorithms developed on healthy data are less accurate for pathological conditions. When evaluating pathological conditions, classifier algorithms developed using data from a target sub-population can restore accuracy to above 95%.Clinical Relevance - This method remotely establishes health-related data of objective outcome measures of activities of daily living

Leveraging Smartphone Sensors for Detecting Abnormal Gait for Smart Wearable Mobile Technologies

Walking is one of the most common modes of terrestrial locomotion for humans. Walking is essential for humans to perform most kinds of daily activities. When a person walks, there is a pattern in it, and it is known as gait. Gait analysis is used in sports and healthcare. We can analyze this gait in different ways, like using video captured by the surveillance cameras or depth image cameras in the lab environment. It also can be recognized by wearable sensors. e.g., accelerometer, force sensors, gyroscope, flexible goniometer, magneto resistive sensors, electromagnetic tracking system, force sensors, and electromyography (EMG). Analysis through these sensors required a lab condition, or users must wear these sensors. For detecting abnormality in gait action of a human, we need to incorporate the sensors separately. We can know about one's health condition by abnormal human gait after detecting it. Understanding a regular gait vs. abnormal gait may give insights to the health condition of the subject using the smart wearable technologies. Therefore, in this paper, we proposed a way to analyze abnormal human gait through smartphone sensors. Though smart devices like smartphones and smartwatches are used by most of the person nowadays. So, we can track down their gait using sensors of these intelligent wearable devices

Implementação e comparação de algoritmos de detecção de passos para pulseiras inteligentes

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2018.Para garantir um acompanhamento médico eficaz, torna-se imprescindível a observação dos as- pectos físicos de um paciente. Com recursos humanos limitados e um grande número de pacientes opta-se por priorizar o espaço físico nos hospitais para pacientes mais graves, todavia o acompa- nhamento médico mantém-se necessário nos demais casos. Desta forma, utiliza-se da tecnologia vestível de modo a permitir um acompanhamento remoto. De modo a verificar e monitorar parâ- metros reais relacionados ao estado de saúde de um paciente, de forma remota, explora-se o uso de um sistema microeletrônico para verificar a detecção de passos em um dispositivo vestível, sendo feito um estudo dos sistemas de contagem de passos embasados na análise de acelerações e velocidade angular dos três eixos do sensor e da relevância do posicionamento do sensor e seu impacto no desempenho. Deste modo, é proposto um algoritmo embasado na estimação de parâmetros do sinal através de técnicas rotacionais de variância, do inglês Estimation of Signal Parametes by Rotational Invariance Techniques (ESPRIT), também implementa-se algoritmos de detecção de picos, análise da FFT para aceleração, análise da FFT para velocidade angular de forma a verificar o desempenho individual de cada técnica. Leva-se em consideração o posicio- namento anatômico do sensor (tornozelo e punho) e a velocidade de locomoção de forma a eleger o algoritmo mais adequado para a implementação em uma pulseira inteligente.To ensure effective medical follow-up, it’s important to observe the physical aspects of a patient. With limited human resources and a large number of patients, the physical space in hospitals are reserved for more serious patients, but medical follow-up is still necessary in all other cases. To facilitate the follow-up, wearable technology is used to allow remote monitoring of the patients. A microelectronic system is used in order to verify and monitor real parameters related to the health status of a patient. It is possible to verify remotely the detection of steps and analyze the data using a wearable device. An analysis of angular acceleration and velocity of the three axes approach is utilized considering the position of the sensor and its impact on performance. SPRIT based algorithms, peak detection, FFT analysis for acceleration, FFT analysis for angular velocity are implemented to verify their results. The anatomical position of the sensor (ankle or wrist), locomotion velocity and sensor data sampling rate are considered in order to choose the most suitable algorithm to implement in a smart watch

A 'one-size-fits-most' walking recognition method for smartphones, smartwatches, and wearable accelerometers

The ubiquity of personal digital devices offers unprecedented opportunities to study human behavior. Current state-of-the-art methods quantify physical activity using 'activity counts,' a measure which overlooks specific types of physical activities. We proposed a walking recognition method for sub-second tri-axial accelerometer data, in which activity classification is based on the inherent features of walking: intensity, periodicity, and duration. We validated our method against 20 publicly available, annotated datasets on walking activity data collected at various body locations (thigh, waist, chest, arm, wrist). We demonstrated that our method can estimate walking periods with high sensitivity and specificity: average sensitivity ranged between 0.92 and 0.97 across various body locations, and average specificity for common daily activities was typically above 0.95. We also assessed the method's algorithmic fairness to demographic and anthropometric variables and measurement contexts (body location, environment). Finally, we have released our method as open-source software in MATLAB and Python.Comment: 39 pages, 4 figures (incl. 1 supplementary), and 5 tables (incl. 2 supplementary

Навігаційна система числення шляху на основі мобільного телефону

Робота публікується згідно наказу ректора від 27.05.2021 р. №311/од "Про розміщення кваліфікаційних робіт вищої освіти в репозиторії університету". Керівник дипломної роботи: д.т.н., проф. кафедри авіаційних комп’ютерно-інтегрованих комплексів, Мухіна Марина ПетрівнаУ сучасному світі, що динамічно розвивається, людина займає провідні позиції, в таких умовах розвивається ринок складних автоматизованих інтегрованих систем підприємств та бізнес-установ різного профілю, а також пристроїв, що забезпечують зв'язок і завжди неминуче знаходяться поруч з нами. Пристрої, які нас оточують всюди, мають не лише набір стандартних програм, які розробник там запровадив. Наприклад, люди, які використовують стандартні методи навігації GPS, ніколи не замислювались про те, як орієнтуватися в торговому центрі, магазині чи на підприємстві. Усі ці організації можуть мати різний розмір за різними будівельними схемами, починаючи від малого бізнесу з кількома десятками людей і закінчуючи великими корпораціями з десятками тисяч працівників. Система інерціальної індивідуальної навігації вирішує такі проблеми, такі системи призначені для вирішення проблем як підприємства в цілому, так і рівня кожної людини. Метою цієї роботи було розробити інерційну пішохідну систему для підрахунку зроблених кроків, а також орієнтації в просторі без зовнішніх даних.In the modern dynamically developing world, a person occupies a leading position, in such conditions the market for complex automated integrated systems of enterprises and business institutions of various profiles, as well as devices that provide communication and always inevitably be near us, is developing. The devices that surround us everywhere have not only a set of standard programs that the developer has introduced there. For example, people who use standard GPS navigation methods never thought about how to navigate in a shopping center and a store, or an enterprise. All of these organizations can be of very different sizes with a variety of construction schemes, ranging from small businesses with a few dozen people to large corporations with tens of thousands of employees. The system of inertial individual navigation solves such problems, such systems are designed to solve the problems of both the enterprise as a whole and the level of each individual. The purpose of this work was to develop an inertial pedestrian system for counting the steps taken, as well as orientation in space without external data

적분 및 매개변수 기법 융합을 이용한 스마트폰 다중 동작에서 보행 항법

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 기계항공공학부, 2020. 8. 박찬국.In this dissertation, an IA-PA fusion-based PDR (Pedestrian Dead Reckoning) using low-cost inertial sensors is proposed to improve the indoor position estimation. Specifically, an IA (Integration Approach)-based PDR algorithm combined with measurements from PA (Parametric Approach) is constructed so that the algorithm is operated even in various poses that occur when a pedestrian moves with a smartphone indoors. In addition, I propose an algorithm that estimates the device attitude robustly in a disturbing situation by an ellipsoidal method. In addition, by using the machine learning-based pose recognition, it is possible to improve the position estimation performance by varying the measurement update according to the poses. First, I propose an adaptive attitude estimation based on ellipsoid technique to accurately estimate the direction of movement of a smartphone device. The AHRS (Attitude and Heading Reference System) uses an accelerometer and a magnetometer as measurements to calculate the attitude based on the gyro and to compensate for drift caused by gyro sensor errors. In general, the attitude estimation performance is poor in acceleration and geomagnetic disturbance situations, but in order to effectively improve the estimation performance, this dissertation proposes an ellipsoid-based adaptive attitude estimation technique. When a measurement disturbance comes in, it is possible to update the measurement more accurately than the adaptive estimation technique without considering the direction by adjusting the measurement covariance with the ellipsoid method considering the direction of the disturbance. In particular, when the disturbance only comes in one axis, the proposed algorithm can use the measurement partly by updating the other two axes considering the direction. The proposed algorithm shows its effectiveness in attitude estimation under disturbances through the rate table and motion capture equipment. Next, I propose a PDR algorithm that integrates IA and PA that can be operated in various poses. When moving indoors using a smartphone, there are many degrees of freedom, so various poses such as making a phone call, texting, and putting a pants pocket are possible. In the existing smartphone-based positioning algorithms, the position is estimated based on the PA, which can be used only when the pedestrian's walking direction and the device's direction coincide, and if it does not, the position error due to the mismatch in angle is large. In order to solve this problem, this dissertation proposes an algorithm that constructs state variables based on the IA and uses the position vector from the PA as a measurement. If the walking direction and the device heading do not match based on the pose recognized through machine learning technique, the position is updated in consideration of the direction calculated using PCA (Principal Component Analysis) and the step length obtained through the PA. It can be operated robustly even in various poses that occur. Through experiments considering various operating conditions and paths, it is confirmed that the proposed method stably estimates the position and improves performance even in various indoor environments.본 논문에서는 저가형 관성센서를 이용한 보행항법시스템 (PDR: Pedestrian Dead Reckoning)의 성능 향상 알고리즘을 제안한다. 구체적으로 보행자가 실내에서 스마트폰을 들고 이동할 때 발생하는 다양한 동작 상황에서도 운용될 수 있도록, 매개변수 기반 측정치를 사용하는 적분 기반의 보행자 항법 알고리즘을 구성한다. 또한 타원체 기반 자세 추정 알고리즘을 구성하여 외란 상황에서도 강인하게 자세를 추정하는 알고리즘을 제안한다. 추가적으로 기계학습 기반의 동작 인식 정보를 이용, 동작에 따른 측정치 업데이트를 달리함으로써 위치 추정 성능을 향상시킨다. 먼저 스마트폰 기기의 이동 방향을 정확하게 추정하기 위해 타원체 기법 기반 적응 자세 추정을 제안한다. 자세 추정 기법 (AHRS: Attitude and Heading Reference System)은 자이로를 기반으로 자세를 계산하고 자이로 센서오차에 의해 발생하는 드리프트를 보정하기 위해 측정치로 가속도계와 지자계를 사용한다. 일반적으로 가속 및 지자계 외란 상황에서는 자세 추정 성능이 떨어지는데, 추정 성능을 효과적으로 향상시키기 위해 본 논문에서는 타원체 기반 적응 자세 추정 기법을 제안한다. 측정치 외란이 들어오는 경우, 외란의 방향을 고려하여 타원체 기법으로 측정치 공분산을 조정해줌으로써 방향을 고려하지 않은 적응 추정 기법보다 정확하게 측정치 업데이트를 할 수 있다. 특히 외란이 한 축으로만 들어오는 경우, 제안한 알고리즘은 방향을 고려해 나머지 두 축에 대해서는 업데이트 해줌으로써 측정치를 부분적으로 사용할 수 있다. 레이트 테이블, 모션 캡쳐 장비를 통해 제안한 알고리즘의 자세 성능이 향상됨을 확인하였다. 다음으로 다양한 동작에서도 운용 가능한 적분 및 매개변수 기법을 융합하는 보행항법 알고리즘을 제안한다. 스마트폰을 이용해 실내를 이동할 때에는 자유도가 크기 때문에 전화 걸기, 문자, 바지 주머니 넣기 등 다양한 동작이 발생 가능하다. 기존의 스마트폰 기반 보행 항법에서는 매개변수 기법을 기반으로 위치를 추정하는데, 이는 보행자의 진행 방향과 기기의 방향이 일치하는 경우에만 사용 가능하며 일치하지 않는 경우 자세 오차로 인한 위치 오차가 크게 발생한다. 이러한 문제를 해결하기 위해 본 논문에서는 적분 기반 기법을 기반으로 상태변수를 구성하고 매개변수 기법을 통해 나오는 위치 벡터를 측정치로 사용하는 알고리즘을 제안한다. 만약 기계학습을 통해 인식한 동작을 바탕으로 진행 방향과 기기 방향이 일치하지 않는 경우, 주성분 분석을 통해 계산한 진행방향을 이용해 진행 방향을, 매개변수 기법을 통해 얻은 보폭으로 거리를 업데이트해 줌으로써 보행 중 발생하는 여러 동작에서도 강인하게 운용할 수 있다. 다양한 동작 상황 및 경로를 고려한 실험을 통해 위에서 제안한 방법이 다양한 실내 환경에서도 안정적으로 위치를 추정하고 성능이 향상됨을 확인하였다.Chapter 1 Introduction 1 1.1 Motivation and Background 1 1.2 Objectives and Contribution 5 1.3 Organization of the Dissertation 6 Chapter 2 Pedestrian Dead Reckoning System 8 2.1 Overview of Pedestrian Dead Reckoning 8 2.2 Parametric Approach 9 2.2.1 Step detection algorithm 11 2.2.2 Step length estimation algorithm 13 2.2.3 Heading estimation 14 2.3 Integration Approach 15 2.3.1 Extended Kalman filter 16 2.3.2 INS-EKF-ZUPT 19 2.4 Activity Recognition using Machine Learning 21 2.4.1 Challenges in HAR 21 2.4.2 Activity recognition chain 22 Chapter 3 Attitude Estimation in Smartphone 26 3.1 Adaptive Attitude Estimation in Smartphone 26 3.1.1 Indirect Kalman filter-based attitude estimation 26 3.1.2 Conventional attitude estimation algorithms 29 3.1.3 Adaptive attitude estimation using ellipsoidal methods 30 3.2 Experimental Results 36 3.2.1 Simulation 36 3.2.2 Rate table experiment 44 3.2.3 Handheld rotation experiment 46 3.2.4 Magnetic disturbance experiment 49 3.3 Summary 53 Chapter 4 Pedestrian Dead Reckoning in Multiple Poses of a Smartphone 54 4.1 System Overview 55 4.2 Machine Learning-based Pose Classification 56 4.2.1 Training dataset 57 4.2.2 Feature extraction and selection 58 4.2.3 Pose classification result using supervised learning in PDR 62 4.3 Fusion of the Integration and Parametric Approaches in PDR 65 4.3.1 System model 67 4.3.2 Measurement model 67 4.3.3 Mode selection 74 4.3.4 Observability analysis 76 4.4 Experimental Results 82 4.4.1 AHRS results 82 4.4.2 PCA results 84 4.4.3 IA-PA results 88 4.5 Summary 100 Chapter 5 Conclusions 103 5.1 Summary of the Contributions 103 5.2 Future Works 105 국문초록 125 Acknowledgements 127Docto

A Novel Walking Detection and Step Counting Algorithm Using Unconstrained Smartphones

Recently, with the development of artificial intelligence technologies and the popularity of mobile devices, walking detection and step counting have gained much attention since they play an important role in the fields of equipment positioning, saving energy, behavior recognition, etc. In this paper, a novel algorithm is proposed to simultaneously detect walking motion and count steps through unconstrained smartphones in the sense that the smartphone placement is not only arbitrary but also alterable. On account of the periodicity of the walking motion and sensitivity of gyroscopes, the proposed algorithm extracts the frequency domain features from three-dimensional (3D) angular velocities of a smartphone through FFT (fast Fourier transform) and identifies whether its holder is walking or not irrespective of its placement. Furthermore, the corresponding step frequency is recursively updated to evaluate the step count in real time. Extensive experiments are conducted by involving eight subjects and different walking scenarios in a realistic environment. It is shown that the proposed method achieves the precision of 93.76 % and recall of 93.65 % for walking detection, and its overall performance is significantly better than other well-known methods. Moreover, the accuracy of step counting by the proposed method is 95.74 % , and is better than both of the several well-known counterparts and commercial products

Body-worn accelerometer-based health assessment algorithms for independent living older adults

The mainstream smart wearable products used for activity trackers have experienced significant growth recently. Among the older population, collecting long periods of activity data in a real-life setting is challenging even with wearable devices. Studies have found inconsistent and lower accuracies when older adults use these smart devices [1], [2],[2],[3]. As a person ages, many have lower daily levels of activity and their dynamic functional patterns, such as gaits and sit-to-stand transitional movements vary throughout the day. This thesis explores wearable health-tracking applications by evaluating daytime and nighttime pattern metrics calculated from continuous accelerometer signals. These signals were collected externally from the upper trunk of the body in an independent-living environment of 30 elderly volunteers. Our gold standard to validate the metrics from the accelerometer signals were similar metrics calculated from an in-home sensor network [4]. This thesis first developed an algorithm to count steps and another algorithm to detect stand-to-sit and sit-to-stand (STS) to demonstrate the importance of considering differences in daily functional health patterns when creating algorithms. Next, this thesis validates that accelerometer data can show similar motion density results as motion sensor data. And thirdly, this thesis proposes an updated vacancy algorithm using a new motion sensor system that detects when no one is in the living space, compared against the current algorithm.Includes bibliographical references (pages 108-111)

"Wearables" in der Diagnostik Multipler Sklerose

Wearables sind meist kleine, am Körper getragene Geräte, die unterschiedliche Parameter erfassen können. Sie stellen auf Grund ihres Zugangs zu lernfähigen Algorithmen und künstlicher Intelligenz eine neue Möglichkeit der Erfassung und Auswertung von Gesundheitsdaten dar. Sie können einen entscheidenden Beitrag zum Verständnis und zur Diagnostik von Multiple Sklerose leisten und Pathomechanismen offenbaren. Die Anwendung einzelner Wearables insb. Bewegungssensoren wurde bereits in mehreren Studien an MS Patienten untersucht. Die Autoren waren sich dabei größtenteils über die vielversprechende klinische Signifikanz der Wearables einig. Eine Studie, die mehrere Wearables gleichzeitig einsetzte, wurde allerdings bis dato noch nicht durchgeführt. Das Ziel dieser Arbeit war es herauszufinden, ob sich durch die Nutzung handelsüblicher Wearables und smart devices gesundheitsrelevante Informationen sammeln lassen, die einen validen Eindruck über den tatsächlichen Gesundheitszustand des Probanden liefern. Dafür haben wir verschiedene Smartwatches, Blutdruckmessgeräte, Waagen und EKGs unterschiedlicher Hersteller an gesunden Probanden und Patienten getestet und mit dem jeweiligen Standard verglichen. Wir haben neun verschiedene Blutdruckmessgeräte an 179 freiwilligen Probanden getestet und mit dem Ergebnis auskultatorischer Messungen, welche zurzeit den etablierten Standard für Blutdruckmessungen darstellen, verglichen. Zur statistischen Auswertung haben wir paired Student’s t-Tests, Korrelationskoeffizient nach Pearson und die Bland-Altman-Methode für den Vergleich unterschiedlicher klinischer Messmethoden herangezogen. Zwei der getesteten Geräte (Beurer BM95, iHealth) zeigten eine große Übereinstimmung mit unserer Referenz und überzeugten durch eine einfache Handhabung. Um die diagnostische Verwertbarkeit von tragbaren EKG-Geräten hinsichtlich der Erkennung von VHF zu untersuchen, haben wir fünf unterschiedliche EKG-Geräte an 25 verschiedenen Patienten mit unterschiedlichen HRST untersucht. Die Aufzeichnungen wurden mit einem Referenzgerät verglichen und auf ihre Übereinstimmung hin überprüft. Zur Auswertung wurden Sensitivität, Spezifität, PPW, NPW, sowie die Bland-Altman-Methode verwendet. Als praktisches Gerät mit der höchsten Sensitivität (86%) stellte sich Kardia Mobile heraus. Fünf verschiedene Fitnessarmbänder wurden mit Hilfe eines Probanden in den Funktionen Schrittzahlmessung, Distanzmessung, Herzfrequenzmessung in Ruhe, sowie unter Belastung getestet. Als Referenz der jeweiligen Funktionen dienten eine Zählungen der Schritte, eine geeichte Tartanbahn und ein Ergometer-EKG. Die statistische Auswertung der Schrittzahl- und Distanzmessung erfolgte mittels ANOVA. Die Auswertung der Herzfrequenzmessung erfolgte mit Hilfe der Bland-Altman Analyse. In der Zusammenschau stellte sich das Gerät Polar A370 als geeignet heraus, in allen unterschiedlichen Funktionen valide Daten messen zu können. Vier verschiedene smarte Waagen wurden mit einer geeichten Referenz-Waage für medizinische Zwecke verglichen. Die Bland-Altman-Analyse wurde zur statistischen Auswertung genutzt. Die besten statistischen Ergebnisse erreichten iHealth Lina HS2 und PICOOC Smart Body Analyzer. Diese Arbeit zeigt, dass Wearables und smart devices valide Gesundheitsdaten an gesunden Probanden sammeln können. Der Nachweis valider Messungen an Multiple Sklerose Patienten und in klinischen Studien muss in zukünftigen Forschungsarbeiten erbracht werden. Weitere technologische Fortschritte werden sicherlich die klinische Relevanz der Geräte erhöhen und in naher Zukunft praktische und kostengünstige Optionen für die dauerhafte Überwachung unterschiedlicher Parameter im ambulanten Umfeld darstellen. Möglicherweise kann auch der Formfaktor der Geräte reduziert werden, so dass beispielsweise auch Blutdruckmessgeräte unterwegs getragen werden könnten. Es wäre ein weiterer Schritt in Richtung dezentralisierter klinischer Studien, die die Effizienz von Therapien in einer realen Umgebung überwachen können