Ambient health monitoring: the smartphone as a body sensor network component

Inertial measurement units used in commercial body sensor networks (e.g. animation suits) are inefficient, difficult to use and expensive when adapted for movement science applications concerning medical and sports science. However, due to advances in micro-electro mechanical sensors, these inertial sensors have become ubiquitous in mobile computing technologies such as smartphones. Smartphones generally use inertial sensors to enhance the interface usability. This paper investigates the use of a smartphone’s inertial sensing capability as a component in body sensor networks. It discusses several topics centered on inertial sensing: body sensor networks, smartphone networks and a prototype framework for integrating these and other heterogeneous devices. The proposed solution is a smartphone application that gathers, processes and filters sensor data for the purpose of tracking physical activity. All networking functionality is achieved by Skeletrix, a framework for gathering and organizing motion data in online repositories that are conveniently accessible to researchers, healthcare professionals and medical care workers

PEMBUATAN SISTEM WEARABLE PEDOMETER DENGAN DISPLAY PADA SISTEM ANDROID

Osteoporosis terjadi karena kekurangan penyerapan kalsium 50% setiap harinya, salah satu solusi pencegahannya dengan cara berjalan kaki sesuai dengan standart kesehatan tiap hari sehingga dapat meningkatkan penyerapan kalsium. Untuk mengetahui jumlah langkah kaki seseorang setiap harinya dapat menggunakan alat penghitung langkah kaki atau pedometer. Beberapa pedometer terkadang salah dalam mencatat pergerakan seseorang misalnya ketika seseorang membungkuk, mengikat tali sepatu atau goncangan lain. Oleh karena itu dalam Tugas Akhir ini kami telah membuat suatu alat yang berfungsi untuk mendeteksi dan menghitung langkah kaki seseorang dengan metode penghitungan gerak kaki dan perpindahan tubuh seseorang. Alat ini menggunakan sensor FSR (Force Sensitive Resistance) untuk mendeteksi pergerakan dari telapak kaki dan Accelerometer untuk mendeteksi pergerakan dan perpindahan seseorang ketika berjalan. Output dari kedua sensor tersebut berupa data analog, sehingga dibutuhkan ADC (Analog to Digital Converter) untuk mengkonversikan data yang didapat tersebut ke dalam data digital dengan menggunakan Mikrokontroller ATMega328. Ketika sensor FSR dan Accelerometer membaca pergerakan atau memberikan nilai ADC tertentu, maka Mikrokontroller ATMega328 akan mengkonversi dan akan dihitung hingga nilai maksimal. Hasil counter tersebut kemudian dikirimkan ke Smartphone melalui Bluetooth HC-05. Pada aplikasi Android tersebut dapat menampilkan jumlah langkah kaki seseorang. Hasil pengujian dari alat ini didapatkan bahwa nilai error dari sistem wearable pedometer ini sebesar 2,75%. Ketika sistem wearable pedometer diuji pada 5 pengguna dengan ukuran kaki yang berbeda-beda maka error yang didapatkan adalah 11,15%

Feature extraction and feature selection in smartphone-based activity recognition

Nowadays, smartphones are gradually being integrated in our daily lives, and they can be considered powerful tools for monitoring human activities. However, due to the limitations of processing capability and energy consumption of smartphones compared to standard machines, a trade-off between performance and computational complexity must be considered when developing smartphone-based systems. In this paper, we shed light on the importance of feature selection and its impact on simplifying the activity classification process which enhances the computational complexity of the system. Through an in-depth survey on the features that are widely used in state-of-the-art studies, we selected the most common features for sensor-based activity classification, namely conventional features. Then, in an experimental study with 10 participants and using 2 different smartphones, we investigated how to reduce system complexity while maintaining classification performance by replacing the conventional feature set with an optimal set. For this reason, in the considered scenario, the users were instructed to perform different static and dynamic activities, while freely holding a smartphone in their hands. In our comparison to the state-of-the-art approaches, we implemented and evaluated major classification algorithms, including the decision tree and Bayesian network. We demonstrated that replacing the conventional feature set with an optimal set can significantly reduce the complexity of the activity recognition system with only a negligible impact on the overall system performance

Classification of physical activity from the embedded smartphone sensors: algorithm development and validation

Physical activity classification has grown in importance lately, for reasons such as positioning or health issues. Given the ubiquity of smartphones and the plethora of sensors they contain, these devices have become an extremely useful tool for this task. In that direction, this project provides an algorithm to count steps using the accelerometer of an Android smartphone. This algorithm searches for patterns in the accelerometer’s signal based on the correlation between consecutive fragments of the signal after a pre-processing step that adapts the data, to count steps under relatively unconstrained ways of carrying the smartphone. The accuracy of the designed algorithm is 92.5% using a database of eleven subjects and four different tests for each subject. As some limitations have been found, a plan for improving the algorithm has been introduced, based on the experience acquired.Clasificar actividad física es cada vez más importante, ya sea para posicionamiento o por problemas de salud. Dada la omnipresencia de los smartphones y el conjunto de sensores que contienen, estos aparatos se han convertido en herramientas verdaderamente útiles para ésta tarea. En esta línea, este proyecto proporciona un algoritmo para contar pasos a partir del acelerómetro de un móvil Android. Este algoritmo busca patrones en el señal de acelerometría basándose en la correlación entre fragmentos consecutivos de señal tras un preprocesado para adaptar los datos; para contar pasos con formas de llevar el móvil poco restrictivas. La precision del algoritmo diseñado es de 92.5% usando una base de datos de once sujetos y cuatro pruebas distintas para cada sujeto. Aunque los resultados no son tan buenos como se pretendía, se han planteado unos posibles pasos para mejorar el algoritmo basados en la experiencia adquirida.Classificar l’activitat física és cada cop més important, ja sigui per posicionament o per problemes de salut. Donada l’omnipresència dels smartphones i el conjunt de sensors que contenen, aquests aparells s’han convertit en eines verdaderament útils per aquesta tasca. En aquesta línia, aquest projecte proporciona un algorisme per comptar passos a partir de l’acceleròmetre d’un mòbil Android. Aquest algorisme busca patrons en el senyal d’accelerometria basant-se en la correlació entre fragments consecutius de senyal després d’un pre-processament per adaptar les dades; per comptar passos amb maneres de portar el mòbil poc restrictives. La precisió de l’algorisme dissenyat és de 92.5% fent servir una base de dades d’onze subjectes i quatre proves diferents per cada subjecte. Com s’han trobat certes limitacions, s’han plantejat uns possibles passos per millorar l’algorisme basats en l’experiència adquirida

An inertial motion capture framework for constructing body sensor networks

Motion capture is the process of measuring and subsequently reconstructing the movement of an animated object or being in virtual space. Virtual reconstructions of human motion play an important role in numerous application areas such as animation, medical science, ergonomics, etc. While optical motion capture systems are the industry standard, inertial body sensor networks are becoming viable alternatives due to portability, practicality and cost. This thesis presents an innovative inertial motion capture framework for constructing body sensor networks through software environments, smartphones and web technologies. The first component of the framework is a unique inertial motion capture software environment aimed at providing an improved experimentation environment, accompanied by programming scaffolding and a driver development kit, for users interested in studying or engineering body sensor networks. The software environment provides a bespoke 3D engine for kinematic motion visualisations and a set of tools for hardware integration. The software environment is used to develop the hardware behind a prototype motion capture suit focused on low-power consumption and hardware-centricity. Additional inertial measurement units, which are available commercially, are also integrated to demonstrate the functionality the software environment while providing the framework with additional sources for motion data. The smartphone is the most ubiquitous computing technology and its worldwide uptake has prompted many advances in wearable inertial sensing technologies. Smartphones contain gyroscopes, accelerometers and magnetometers, a combination of sensors that is commonly found in inertial measurement units. This thesis presents a mobile application that investigates whether the smartphone is capable of inertial motion capture by constructing a novel omnidirectional body sensor network. This thesis proposes a novel use for web technologies through the development of the Motion Cloud, a repository and gateway for inertial data. Web technologies have the potential to replace motion capture file formats with online repositories and to set a new standard for how motion data is stored. From a single inertial measurement unit to a more complex body sensor network, the proposed architecture is extendable and facilitates the integration of any inertial hardware configuration. The Motion Cloud’s data can be accessed through an application-programming interface or through a web portal that provides users with the functionality for visualising and exporting the motion data