a wearable device for physical activity monitoring

8/2/2019 A Wearable Device for Physical Activity Monitoring

1/4

A Wearable Device for Physical Activity MonitoringWith Built-in Heart Rate Variability

Anh Dinh, Daniel Teng, Li Chen, Seok-Bum Ko, Yang Shi, Carl McCrosky, Jenny Basran, Vanina Del Bello-Hass

University of Saskatchewan, Saskatoon, Canadaemail: [email protected]

Abstract This paper presents the implementation of a system tosense, collect and store physiology activities for the purpose of monitoring the elderly people and the people who are takingmedication. The system includes a wearable device to be worn bythe individual to collect physical activity data, a memory card,and a computer to retrieve and analyze data. A heart beatmeasurement is also included to provide better monitoring.Accelerometer and gyroscope are the main sensing devices. Ahigh capacity SD card is used for data storing. Testing resultsshow the system function properly and provide accurate data formonitoring purposes.

Keywords-physical activity monitoring, accelerometer, gyroscope, heart rate variability, homecare

I. I NTRODUCTIONMost older adults have one or more health problems. Their

day-to-day and long-term attention has great relevance to primary healthcare providers and their loved ones.Identification and tracking of daily physical activity are keyfactors to evaluate the quality of life and health status of those

people [1,2]. Health condition monitoring of aged is necessaryto prevent injuries and guarantee safety social environment inorder that old people can enjoy their social actions [3]. Theability to record and classify the movements of an individual isessential when attempting to determine his or her degree of functional ability and general level of activity. In addition tomonitoring physical activity on the elderly people, there is aneed for the patient health status before and after medicationintake [4-6]. In the past, the practice depends on oldtechniques such as patient diaries, interviews, phone call andself-report using questionnaires. Accuracy of these techniquesis questionable not to mention the high cost and timecommitment of the healthcare personnel.

Numerous physical activity monitoring have beendeveloped in the past. Some of the systems use accelerometersmounted in various locations on the body to record the

physical motions [3,7,8,9], the others uses kinematic sensors[10]. Similar systems can be found using video recording withcomplicated algorithm to determine activities [5].

This paper presents an implementation of a wearable devicefor monitoring of physical activities of the elderly people and

patients who are taking medication. The device detects posturalmovements using a set of sensors consists of a 3-axialaccelerometer, a 2-axial gyroscope, and a heart beat detectioncircuit. The device is worn on the chest of an individual to

sense his or her movements and vital sign. The device iscapable of storing sensor data. Data processing is to beperformed offline, after a recording had been completed.

II. THE DEVICE

One criteria of the design is to have a small, light weightdevice, easy to use by the patients targeting the elderly people.Trapping the device on the chest is the wearing method for thedesign. Low power design is another constraint in thisimplementation since the device intends to be used for anextended period of time. Shown in Figure 1 is the system set-up for monitoring purpose in which the wearable device is tobe worn by an individual, a SD card is to store the physicalactivity and vital sign data. The device consists of a 3-axialaccelerometer, a 2-axial gyroscope to capture the wearerspostural movements, a heart beat sensor to detect heart beatsignal, a data logger to collect physical activity and vital signand to write the data into the SD card. A rechargeable batteryis used to power the device. Healthcare personnel can retrieveall required information from the SD card for monitoring orpre/post-medication analysis. The onboard microcontroller canalso be programmed to detect and record the fall of thepatients or any irregular vital sign.

Figure 1. System set-up includes a wearable device, a 1GB SD card, and acomputer to retrieve and analyze postural movements and heart rate data.

Figure 2 provides a closer look of the wearable device. Thisdevice comprises of a high precision accelerometer sensor (Analog Devices ADXL 330), a gyroscope (InvenSense IDG-300), a heart beat sensing circuit, and a 3.7V rechargeable

battery. The battery is mounted underneath the circuit board. A

Wearable device to be worn on the patient chest ( i.e., sensor node)

Monitoring

978-1-4244-2902-8/09/$25.00 2009 IEEE 1


2/4

battery charging circuit is also built-in on the printed circuit board.

The main postural sensing device used in thisimplementation is the accelerometer, ADXL330, made byAnalog Devices. The ADXL330 is a small, thin, low power,complete 3-axis accelerometer with signal conditioned voltageoutputs, all on a single monolithic IC. The device measuresacceleration with a minimum full-scale range of 3g. It can

measure the static acceleration of gravity in tilt-sensingapplications, as well as dynamic acceleration resulting frommotion, shock, or vibration. The user selects the bandwidth of the accelerometer using the capacitors at the three output x, y,and z. The output bandwidth can be selected to suit theapplication. Bandwidths of 50Hz for all 3-axis outputs have

been selected by bypassing the voltages with 0.1 F capacitors.The accelerometer operates on a single supply from 2.0-3.6Vwith a very low current of 200 A.

Heart beat sensor circuitry

Microphone

Accelerometer andgyroscope

ARM processor 3.7V, 2000mAhLithium Battery

1GB SD card

Figure 2. The wearable device.

Aiding the accelerometer in sensing postural movements, adual-axis gyroscope is installed in the device. The sensor is anintegrated dual-axis gyroscope, IDG-300, made by InvenSense.This MEMS device operates at a single supply voltage of 3.0-3.5V. The sensor provides analog outputs of x and y rates witha full scale of 500 0/sec. The output voltages of the rotationsare connected to the onboard ADC of the microcontroller. Oneof the shortcomings of the gyroscope is its output drifting. Thisis due to the nature of the gyroscope and the error requiresfurther filtering to correct. This rate sensor consumes power continuously due to its long wake-up time. Both theaccelerometer and the gyroscope have very high shock survivability of over 500g. The combination of accelerometer and gyroscope provides a better postural activity measurementas proved in the testing results.

The heart beat sensing consists of an acoustic sensor (amicrophone) to pick up the sound of the heart beats. Themicrophone is attached to the belt which is trapped around thechest at the heart location. This microphone is a low costelectret condenser type having a flat response from dc to 10kHzfor a near field of 6mm. With appropriate amplification andfiltering, the heart beat signal will be obtained using the circuit

block diagram shown in Figure 3. The filtering andamplification circuitries were built using discrete componentson a small PCB. Low power dual-amplifier using a singlesupply was used in the design. A simple single pole high passfilter is used to eliminate DC level of the heart beat signal fromthe bias of the microphone. The low pass filter, which is also asingle pole filter, is used to diminish the noise beyond thenormal frequency of the heart beat which is below 5Hz. Theheart beat sensing circuit provides both analog and digitaloutputs which can be connected to the ADC or digital input of the microcontroller. A mono-stable circuit using a timer IC isused to capture the edge of the heart beat signal. The timer generates a digital signal corresponding to every beat of theheart. The heart beat signal can be very noisy and the echofrom the sound of the beat picked up by the microphone cantrigger the mono-stable circuit which causes error. The ON-time of the timer circuit is adjusted to eliminate such mishap.The heart beat signal is continuously stored to monitor heartrate variability.

Bias

MicrophoneMB4015NSC-3

Mono-stablecircuit

Gain+ HPF(0.5Hz)

Gain+ LPF(6Hz)

Output

Figure 3. Block diagram of the custom built heart beat sensor.

The data logger is a module made by Spark FunElectronics, the Logomatic V1.0. The module includes anARM processor, the LPC2138 made by NXP (Philips). TheLPC2138 microcontroller is based on a 16/32-bit

ARM7TDMI-S CPU with real-time emulation and embeddedtrace support. The microcontroller also has 10 channels 10-bitADC and all are used in the logger module. The channels can

be selected on or off as desired in the logon file stored in theSD card. The file is used to configure the microcontroller uponstart-up. The ADC logging can be in ASCII or binary formatfor ease of use. Sampling rate of the ADC channels can also

be selected to fit particular application without over-samplingor over running the SD card capacity. The sampling frequencyused in this implementation is 50Hz and can be changed in alogon file. Sensor data are logged in the ASCII format; eachmeasurement of the channel is written in and followed by adelimiting character. At the end of each measurement frame, acarriage return and a line feed are placed for further delimiting. A 1GB SD card is used in this application. Thismemory capacity is sufficient for at least 100 hours of continuous logging. On average, the logger draws 75mA froma 3.3V supply.

III. EXPERIMENT R ESULTS One of the wearable devices was trapped on the thorax of a

healthy participant to test the system functionalities includingdata storage, data display and data analysis.

2


3/4

A. Data Collection for Physical Activity and Heart RateVariabilityFigure 4 and Figure 5 show the plots for the three-axis

accelerometer, the rotational data from the dual-axis gyroscopeand the heart beat signal. The data display is for a variety of movements of the wearer during the test. Figure 6 shows anexample of the data stored in the SD card read by the MicrosoftExcel spreadsheet. The sampling rate is 50 samples per second.

In this example, the 1st

to 3rd

columns list the x, y, and z valuesof the accelerometer. The 4 th and 5 th columns store x and yrates of the gyroscope and the last 2 columns contain digitaland analog values of the heart beat signal. The raw data storedin the SD card can now be viewed and processed off-line in acomputer. The size of the data file is to be determined by thedata polling rate and the number of hours to be observed.

B. Data Analyzing Numerous techniques to classify human movement using

accelerometer data have been published [7,11,12,13]. In thisimplementation, machine learning method was used to identify

the fall and some of the simple physical activities. Thisexperiment focuses in using acceleration and gyroscope datato detect a fall since the elderly people are our target group.Test data are classified into 7 different postural movements:standing, walking, lying, forward fall, backward fall, left fall,and right fall. Five algorithms have been used for the machinelearning methods to test the data: Naive Bayes [14], supportvector machine, C4.5, ripple down rule learner, and radial

basis function network. Due to its high accuracy and fastmodel building, the Nave Bayes algorithm was chosen to usein the Java based data mining tool WEKA (WaikatoEnvironment for Knowledge Analysis) [15]. It has also beenobserved that the data from the accelerometer achieved only90% of accuracy but when they are combined with thegyroscope data, the detection probability has increased to97%. This agrees with the finding in [16] in which thesimultaneous use of accelerometer, gyroscope and tilt sensor increases the fall detection accuracy.

Figure 4. Plots of postural movements when the device is worn vertically on the chest. The data is raw data, i.e., accelerometer data are not converted into gravityforce, g, and gyroscope data are not converted into degree/second.

standing-up &sitting-down

1 277 553 829 1105 1381 1657 1933 2209 2485 2761 3037 3313 3589 3865 4141 4417 4693 4969 5245 5521

700

650

600

550

500

450

400

350

600

550

500

450

400

350

1 277 553 829 1105 1381 1657 1933 2209 2485 2761 3037 3313 3589 3865 4141 4417 4693 4969 5245 552 1

walking sitting sittingstanding up &walking

quick standing-up,sitting, and lying

Accelerometer data: xyz

Amplitude

Samples

Gyroscope data: xy

Samples

A mplitude

sitting-up

3


4/4

Figure 5. Plot of the heart beat signal showing a 68 beat-per-minute and azooming in of the heart beat signal with edge detection to convert to the

digital signal.

Figure 6. Data storage includes accelerometer data (x,y,z), gyroscope data(x,y), and heart beat signal (digital and analog).

Test results on a fully-charged battery (2000mAh) showingthe wearable device can properly operate for 26 hours. Thegyroscope and the heart beat circuit use power continuouslywhile the accelerometer can be put into sleep mode if desired.As the battery voltage falls below 3.0V, the gyroscope stopsfunctioning while the other parts of the wearable device stilloperate.

IV. CONCLUSION An off-line physical activity monitoring system was

successfully designed and built. The system is also embeddedwith vital sign for heart rate variability monitoring. Thewearable device and system functionalities were verifiedsuccessfully on a healthy individual. The tests have not been

conducted on the people as the system is designed for. Further experiments are required in order to evaluate the performanceof the system on the target group of people. The system

provides accurate physiological activity data to the needs for monitoring elderly people or people having medication intake.Off-line data process should be improved to wirelesstransmitting plus real-time data process which can be moreuseful for saving life when heart or body meets problems.

ACKNOWLEDGMENT

This project is funded by the Natural Sciences andEngineering Research Council (NSERC) of Canada under Strategic Project Grant number STPGP 350545.

R EFERENCES [1] C. M. Musil et al, Health Problems and Health Actions Among

Community-Dwelling Older Adults: Results of a Health Diary Study, Applied Nursing Research , Vol. 11, No. 3, August 1998, pp. 138-147.

[2] J. K. Wu, L. Dong, and W. Xiao, Real-time Physical ActivityClassification and Tracking using Wearable Sensors, 6 th InternationalConference on Information, Communications & Signal Processing(ICICS), December 2007, pp.1-6.

[3] A. Makikawa, S. Asajima, K. Shibuya, R. Tokue, H. Shinohara,Portable Physical Activity Monitoring System for the Evaluation of Activity of Aged in Daily Life, 2 nd Joint EMBS/BMES Conference,Houston, Texas, USA, October 23-26, 2002, pp. 1908-1909.

[4] A. Batz, M. Batz, N.V. Lobo, M. Shah, A computer Vision System for Monitoring Medication Intake, 2 nd Canada Conference on Computer and Robot Vision (CRV05), May 1-11, 2005.

[5] S. Ammouri and G.A. Bilodeau, Face and Hands Detection andTracking Applied to The Monitoring of Medication Intake, CanadaConference on Computer and Robot Vision (CRV08), May 28-30,2008, pp. 147-154.

[6] D.M. Serrill, R. Hughes, S.S. Salles. T. Lie-Nemeth, M. Akay, D.G.Standeart, P. Bonato, Advanced Analysis of Wearable Sensor Data toAdjust Medication Intake in Patients with Parkinsons Disease, 2 nd International IEEE EMBS Conference on Neural Engineering,Arlington, Virginia, USA, March 16-19, 2005, pp. v-viii.

[7] J. Han, H. Kim, S. Choi, K.S. Park, Indoor Activity MonitoringSystem Using an Accelerometer and ZigBee, 6 th International SpecialTopic Conference on ITAB, Tokyo, Japan, 2007, pp. 177-178.

[8] C.V. Bouten, K.T. Koekkoek, M. Verduin, R. Kodde and J.D. Janssen,A triaxial accelerometer and portable data processing unit for theassessment of daily physical activity, IEEE Trans. Biomed. Eng. , vol.44, no. 3, March 1997, pp. 136147.

[9] M.J. Mathie, A.C.F. Coster, N.H. Lovell, and B.G. Celler, A pilot studyof long term monitoring of human movements in the home usingaccelerometry, J. Telemed. Telecare , vol. 10, 2004, pp. 144151.

[10] B. Najafi, et al, An Ambulatory System for Physical ActivityMonitoring in Elderly, 1 st Annual International IEEE-EMBS SpecialTopic Conference on Microtechnologies in Medicine & Biology, Lyon,France, October 12-14, 2000, pp. 562-566.

[11] L. Dong, J. Wu, X. Chen, Real-Time Physical Activity Monitoring byData Fusion in Body Sensor Networks, 10 th International Conferenceon Information Fusion, July 2007, pp. 1-7.

[12] D.M. Karantonis, M.R. Narayanan, N.H. Lovell, and B.G. Celler,Implementation of a Real-Time Human Movement Classifier Using aTriaxial Accelerometer for Ambulatory Monitoring, IEEE Transactionson Information Technology in Biomedicine, Vol. 10, No. 1, January2006, pp. 156-157.

[13] E. Cordera, A.M. Lopez, D. Alvarez, R.C. Gonzalez, J.C. Alvarez,Physical activity classification from body center of gravityaccelerations, International Conference on Intelligent Environments,

September 2007, pp. 477-480.[14] J. Anderson and M. Matessa, Exploration of an incremental, Bayesianalgorithm for categorization, Machine Learning, 9:275-308, 1992.

[15] http://www.cs.waikato/ac/nz/ml/weka [16] J.Y. Hwang, J.M. Kang, Y.W. Jang, H.C. Kim, Development of novel

algorithm and real-time monitoring ambulatory system using Bluetoothmodule for fall detection in the elderly, IEMBS04, Vol. 3, pp. 2204-2207

0

100

200

300

400

500

600

700

800

900

1000

1 176 351 526 701 876 1051 1226 1401 1576 1751 1926 2101 2276 2451 2626 2801 2976

0

100

200

300

400

500

600

700

800

900

1000

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201

4

a wearable device for physical activity monitoring

Documents