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%

SisFall : A Fall and Movement Dataset

ABSTRACT: Research on fall and movement detection with wearable devices has witnessed promising growth. However, there are few publicly available datasets, all recorded with smartphones, which are insufficient for testing new proposals due to their absence of objective population, lack of performed activities, and limited information. Here, we present a dataset of falls and activities of daily living (ADLs) acquired with a self-developed device composed of two types of accelerometer and one gyroscope. It consists of 19 ADLs and 15 fall types performed by 23 young adults, 15 ADL types performed by 14 healthy and independent participants over 62 years old, and data from one participant of 60 years old that performed all ADLs and falls. These activities were selected based on a survey and a literature analysis. We test the dataset with widely used feature extraction and a simple to implement threshold based classification, achieving up to 96% of accuracy in fall detection. An individual activity analysis demonstrates that most errors coincide in a few number of activities where new approaches could be focused. Finally, validation tests with elderly people significantly reduced the fall detection performance of the tested features. This validates findings of other authors and encourages developing new strategies with this new dataset as the benchmark

SisFall: A Fall and Movement Dataset

Research on fall and movement detection with wearable devices has witnessed promising growth. However, there are few publicly available datasets, all recorded with smartphones, which are insufficient for testing new proposals due to their absence of objective population, lack of performed activities, and limited information. Here, we present a dataset of falls and activities of daily living (ADLs) acquired with a self-developed device composed of two types of accelerometer and one gyroscope. It consists of 19 ADLs and 15 fall types performed by 23 young adults, 15 ADL types performed by 14 healthy and independent participants over 62 years old, and data from one participant of 60 years old that performed all ADLs and falls. These activities were selected based on a survey and a literature analysis. We test the dataset with widely used feature extraction and a simple to implement threshold based classification, achieving up to 96% of accuracy in fall detection. An individual activity analysis demonstrates that most errors coincide in a few number of activities where new approaches could be focused. Finally, validation tests with elderly people significantly reduced the fall detection performance of the tested features. This validates findings of other authors and encourages developing new strategies with this new dataset as the benchmark

Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer

The consequences of a fall on an elderly person can be reduced if the accident is attended by medical personnel within the first hour. Independent elderly people often stay alone for long periods of time, being in more risk if they suffer a fall. The literature offers several approaches for detecting falls with embedded devices or smartphones using a triaxial accelerometer. Most of these approaches have not been tested with the target population or cannot be feasibly implemented in real-life conditions. In this work, we propose a fall detection methodology based on a non-linear classification feature and a Kalman filter with a periodicity detector to reduce the false positive rate. This methodology requires a sampling rate of only 25 Hz; it does not require large computations or memory and it is robust among devices. We tested our approach with the SisFall dataset achieving 99.4% of accuracy. We then validated it with a new round of simulated activities with young adults and an elderly person. Finally, we give the devices to three elderly persons for full-day validations. They continued with their normal life and the devices behaved as expected

Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer

The consequences of a fall on an elderly person can be reduced if the accident is attended by medical personnel within the first hour. Independent elderly people often stay alone for long periods of time, being in more risk if they suffer a fall. The literature offers several approaches for detecting falls with embedded devices or smartphones using a triaxial accelerometer. Most of these approaches have not been tested with the target population or cannot be feasibly implemented in real-life conditions. In this work, we propose a fall detection methodology based on a non-linear classification feature and a Kalman filter with a periodicity detector to reduce the false positive rate. This methodology requires a sampling rate of only 25 Hz; it does not require large computations or memory and it is robust among devices. We tested our approach with the SisFall dataset achieving 99.4% of accuracy. We then validated it with a new round of simulated activities with young adults and an elderly person. Finally, we give the devices to three elderly persons for full-day validations. They continued with their normal life and the devices behaved as expected

Smart Technologies. SmartTech-IC 2022: Third International Conference on Smart Technologies, Systems and Applications

La Universidad Politécnica Salesiana ha estado promoviendo la investigación científica proporcionando financiamiento para el desarrollo y ejecución de propuestas en diversas áreas de investigación. En la Sede Cuenca, se han formado grupos multidisciplinarios para llevar a cabo estas propuestas de investigación. Aunque estos indicadores demuestran resultados favorables en la implementación de una cultura de investigación sólida, todavía se informan proyectos que, debido a varios factores, no logran publicar sus resultados en revistas indexadas. Entre estos factores se encuentran los altos costos de movilidad de los investigadores para presentar sus trabajos en eventos indexados, así como la falta de implementación de criterios adecuados de cienciometría. Esto ha impedido que, en muchos casos, investigaciones con resultados sobresalientes no sean comunicadas a la comunidad científica internacional, lo que no contribuye al aumento de la productividad académica institucional. Además, en algunos casos, se publican trabajos en revistas sin una indexación que contribuya a los indicadores institucionales (Castillo y Powell, 2019) (Guerrero-Casado, 2017)