Luận văn ThS Hanh Ngoc Dang: Phân loại hoạt động thể chất dùng gia tốc kế 3 trục

MASTER THESIS The development of a classification system for monitoring physical activity using a miniaturized 3-axis accelerometer Hanh Ngoc Dang This work has been carried out at Institute of Micro and Nano Systems Technology, under the supervision of Dr. Johannessen, Vestfold University College Prof. Lars Hoff, Vestfold University College Dr. Nourddine Bouhmala, Vestfold University College Dr.

Erik Årsand, Norwegian Center for Telemedicine Horten, 25 May 2012 Submitted to the Faculty of Technology and Maritime Science, Vestfold University College, in partial fulfilment of the requirements for the degree Master of Microsystem Technology. Abstract A miniaturized ambulatory motion detection system that aims to record the different levels of activity from a person has been developed. The aim of this study was to construct a novel k-means based classification algorithm that was able to interpret signals generated from a standard 3-axis silicon accelerometer and to translate these signals into a recognizable cluster of pre-defined activities. The system has focused on identifying three basic classes of activities based on resting, walking and running.

These have been extended to lower level sub-classes such as different postures of resting (lying, sitting and standing), different pace of walking (slow, moderate, fast, up and down stairs) and running (jogging, slow, moderate and full speed). The developed system have given encouraging results with a 100% success rate of classification of the three basic classes of activities, and a 84% success rate for the lower level of different pace of walking and running. It also showed that different postures of resting as well as walking forward and walking up and down stairs can be distinguished with an accuracy of 90%. The potential extension towards self monitoring systems for people suffering from diabetes mellitus has been considered by converting the activities into metabolic equivalents that will help predict the associated energy expenditure.

By calculating the energy consumption over a given time interval, the motion detection system could be used as a supplementary tool that help predicts the glucose level as a function of energy intake and energy spent. Preface This multidisciplinary project is related to the scientific fields of microsystem technology, telemedical systems, digital signal processing and data mining, and I were happy to receive support from my supervisors from these different departments. I would like to thank my supervisors, Erik Johannessen, Lars Hoff, and Nourddine Bouhmala for your support, advices and inspiration. Further I would also like thank Eirik Årsand at the Norwegian Centre for Telemedicine, for valuable input on the potential application side.

I did also appreciate the support of the five people who participated in the experiment of collecting data for our project. I want to say thank you to all of them. Finally I also have to thank my family and my boyfriend for being with me through all this tough time. Abbreviations a acceleration ANN artificial neural networks ASK Amplitude-shift keying AR autoregressive BG blood glucose BMR Basal metabolic rate corr cross-axis correlation d Euclidean distance DMP digital motion processor E spectral energy EE Energy expenditure FFT Fast Fourier Transform FSK Frequency-Shift Keying GFSK Gaussian Frequency-Shift Keying GPS Global Positioning System I2C Inter-Integrated Circuit IMA Integral of the Modulus of body Acceleration KNN k-nearest neighbour LE low energy LHS left hand side m mass MEMS micro-electromechanical systems MET Metabolic equivalent of a task MSK Minimum-shift keying MRMR Minimum Redundancy Maximum Relevance NI National Instruments OOK On/Off Keying p probability PA physical activity PC personal computer QFN Quad-flat no-leads RF radio frequency rms root mean square RMR resting metabolic rate s standard deviation SPI synchronous peripheral interface SMA signal-magnitude area SNR signal-to-noise ratio SSE error Sum of Square SVM support vector machines SVs support vectors TA, θ tilt angle USCI universal serial communication interface v velocity W work Contents Abstract.1 Sensors – 3‐axis CMA 3000‐D01 .2 Microcontroller ‐ CC430F6137IRGC .3 Wireless Communication ‐ CC1101 sub‐1‐GHz .5 Computer and Software.

Materials and Methods .2 Enhanced K‐means clustering .4 Estimation of Energy Consumption .3 Noise reduction results .4 Features extraction results .2 Classification of motion .1 Trial system‐ classify 3 classes of activities .2 Trial system 2‐classify different pace of walking and running.3 Trial system 3‐classify different postures of resting .4 Trial system 4 ‐distinguish walk up/down stairs from walk straight forward.3 Estimated Energy Consumption.1 Results versus Objectives.2 Limitation of the device .3 Limitations of the system.4 Limitation of the MET model .5 Comparison to prior art/literature.1 Extend the range of activities .2 Free‐living performance test.4 Improve the algorithm .5 Packaging and assembly .6 Implementation into an electronic diabetes diary .1 Load waveform signals into Matlab.6 Trial System 2, 3 and 4 .7 Assessment of energy expenditure. Introduction Motivation: Lifestyle related chronical diseases have in part been related to the lack of physical activity from which our bodies were originally designed to endure. The combination with unhealthy diets is further corroborating to the global burden of disease, death and disability. Combating lifestyle related diseases have been based on both diet and exercise, in which the latter have focused on step counters, pedometers and heart rate sensors as a tool of self-monitoring.

However, the technology holds the potential for future integration together with other sensor technologies as part of a therapeutic regime. MEMS and BioMEMS have a prerogative for miniaturisation and automation to such an extent that it can be integrated in wearable devices based on watches and ultimately miniaturised implantable sensor systems. A target user group is the growing number of people suffering from diabetes mellitus in which physical activity is paramount of maintaining a healthy glycaemic control that reduce long term detrimental effects, as well as preventing the onset of the disease in people diagnosed with prediabetes and impaired glucose tolerance. Diabetes is a metabolic disorder that results in abnormally elevated or suppressed blood glucose (BG) values due to the inability or reduced ability of the body to metabolize glucose.

Diabetes is classified into Type 1 which is affecting 5-10% of the diabetic population as well as 1 in 500 children under the age of 18 [1]. The autoimmune destruction of the β-cell of the pancreas [2], result in insulin not being produced. This type of diabetes require frequent daily monitoring of the BG level as well as daily injections of insulin. Type 2 is the most common type of diabetes which affects around 90- 95% of the diabetic population.

This type is characterized by insulin production still being sustained, but where the hormone has either lost its ability to regulate BG or that its production has become too low. Quite often are both conditions present at the same time [3]. The development of the Type 2 condition is governed by genetic factors, ethics, overweight, decreased physical activity, an aging population and diet [4]. Therapeutic regimes that treats diabetes aims to maintain a constant level of BG that targets the level in healthy subjects of approximately 90 mg/dl (5 mM).

This is achieved by taking frequent measurements with an external blood glucose monitor that samples a drop of blood from the fingertip. Insulin is used to reduce an elevated level of BG (hyperglycemia) which is a result of nutritional intake, whereas extra sugar (nutritional) intake is used to raise the BG if the level is found to be too low (hypoglycaemia). However, in order to avoid the occurrence of hyper- and hypoglycaemic events, a functional therapeutic regime would need to predict a potential rise or fall in BG before it actually occurs. Hypothesis: Although diet plays an important role in maintaining stable levels of BG, physical activity has the added benefit of preventing an unwanted rise in BG by burning off excess glucose available in the blood stream.

By monitoring physical activity one can seek to estimate the energy consumption per unit mass at a given time. This instant energy consumption can be integrated over a desired time interval that relates the consumption with the energy intake. This represents a powerful tool that may enable a prediction of the current BG value as well as illustrating potential trends of this BG value is rising or falling if the current level of activity is maintained. 6 Problem: Single 1-axis step counters will only provide static information of an estimated distance and are unable to distinguish if you have been running, walking or cycling this distance.

More sophisticated motion detectors that are able to distinguish between different classes of activities in real time would provide a much more comprehensive picture of the activity which can be related to a given energy consumption. The therapeutic effect of the motion detector will only be effective if this device can be integrated with a system that also monitors the nutritional intake and performs direct measurements of the BG as control. Hence the motion detector should be ultra compact and portable in nature in order to enable this integration. Current standard MEMS based motion sensors from the silicon microfabrication industry have size constraints in the mm regime and could be potential low cost candidates for such a device.

Aim: The aim of this project is to investigate if motion data from a standard 3-axis accelerometer may form the basis for a simple classification algorithm that is able to distinguish between a number of preset classes of activities such as standing, walking and running, and thereby extending the range of activities compared to that achieved in prior art. After performing the initial classification, the algorithm will attempt grading these activities from e. slow, normal and brisk walk/run, and walking up/down stairs and different postures of resting. The different classes of activity will be related to a preset value of energy consumption per unit weight.

By adding the body weight of the person, the total work (energy consumption) related to a series of activities within a given time frame will be presented as the final output. The project will seek to (i) integrate the components of the application protocol into a suitable package, and (ii) translate the data from the accelerometer using an algorithm. Re- calibrations to prevent sensor-drift induced errors will be performed before using the device. The project builds on existing activities in biomedical microsystems at Vestfold University College (HVE) and in physical activity monitoring for people with diabetes at Norwegian Centre for Telemedicine (NST).1 Motion detectors This project will target a miniaturised motion detector suitable for integration with related sensor applications such as blood glucose monitoring and dietary intake as support of a therapeutic regime.

The evolution of motion detectors is reviewed from the basic mechanical pedometer to the electronic accelerometer. The final prototype; front view and side view [5]. 7 Single axis motion sensors: Pedometers were originally designed on 1-axis motion sensors that both minimized the power consumption and the microcontroller based programming efforts that was implemented in step counters [5, 6] or pedometers [7, 8]. These are on-body sensing devices that typically measure the number of “steps” an individual takes in a continuous manner.

Those devices are usually attached to the belt on the hip [5, 6] and have a built-in LCD panel that displays the number of steps taken, or are included in MP3 players or mobile phones that can estimate the distance walked and as well as a crude approximation of the energy expenditure. For example, prior art (see Fig 1) designed by the Norwegian Centre for Telemedicine (NST) were based on a 1-axis step counter featuring an integrated microcontroller harvesting data, a Bluetooth adapter for wireless data transfer and a battery capable of delivering the peak power demand of 40 mA, all packaged into an enclosure measuring 6x4x1 cm3 [5, 6]. Although these devices are small and inexpensive and have been widely used in healthcare research and clinical experiments, they have their own limitations. They can only record a limited set of activities (i.

distinguishing walking from resting). Most pedometers estimate the energy expenditure by multiplying the number of steps with an energy coefficient. Therefore these devices will not be able to estimate the user’s energy expenditure from a wide range of different activities.