A wearable, multi-modal sensor is presented that can non-invasively monitor a patient’s activity level and heart function concurrently for more than a week. The 4in² sensor incorporates both a non-contact heartrate sensor and a 5-axis inertial measurement unit (IMU), allowing simultaneous heart, respiration, and movement monitoring without requiring physical contact with the skin [1]. Hence, this Oregon State University Life and Activity Monitor (OLAM) provides the unique opportunity to combine motion data with heart-rate information, enabling assessment of actual physical activity beyond conventional movement sensors. OLAM also provides a unique platform for non-contact sensing, enabling the filtering of movement artifacts generated by the non-contact capacitive interface, using the IMU data as a movement noise channel. Intended to be used in clinical trials for weeks at a time with no physician intervention, the OLAM allows continuous noninvasive monitoring of patients, providing the opportunity for long-term observation into a patient’s physical activity and subtle longitudinal changes

Albright, Ryan K.

Cauwenbergh, G.

Chi, Mike Y.

Chiang, Patrick Y.

Goska, Benjamin J.

Hagen, Tory M.

Undetermined

ScholarsArchive@OSU

1OLAM: A Wearable, Non-Contact Sensor forContinuous Heart-Rate and Activity MonitoringRyan K. Albright∗, Member, IEEE, Benjamin J. Goska∗ Member, IEEE, Tory M. Hagen†, Mike Y. Chi‡Member, IEEE, G. Cauwenberghs§ Member, IEEE, Patrick Y. Chiang∗ Member, IEEEAbstract—A wearable, multi-modal sensor is presented thatcan non-invasively monitor a patient’s activity level and heartfunction concurrently for more than a week. The 4in2 sensorincorporates both a non-contact heartrate sensor and a 5-axis inertial measurement unit (IMU), allowing simultaneousheart, respiration, and movement monitoring without requiringphysical contact with the skin [1]. Hence, this Oregon StateUniversity Life and Activity Monitor (OLAM) provides theunique opportunity to combine motion data with heart-rateinformation, enabling assessment of actual physical activitybeyond conventional movement sensors. OLAM also provides aunique platform for non-contact sensing, enabling the filteringof movement artifacts generated by the non-contact capacitiveinterface, using the IMU data as a movement noise channel.Intended to be used in clinical trials for weeks at a time withno physician intervention, the OLAM allows continuous non-invasive monitoring of patients, providing the opportunity forlong-term observation into a patient’s physical activity andsubtle longitudinal changes.I. INTRODUCTIONContinuous monitoring of patients is becoming increasinglyimportant in personalized healthcare. Conventional data ob-tained from a medical clinic – such as blood tests, salivaswabs, and vital sign measurements (blood pressure, pulserate, respiration) – are typically coarsely captured at infre-quent doctor’s visits, thereby offering a limited glimpse intothe condition of a patient’s health. This paper presents theOregon State University Life and Activity Monitor (OLAM),a low-cost, robust, non-invasive device designed for monitor-ing the activity of patients continuously for weeks at a time.The OLAM is composed of a contactless heart-rate sensorsimilar to [2] coupled with a five-axis inertial measurementunit (IMU). This combination of movement fused with heart-rate data provides a more accurate measurement of activityover these extended periods compared with sensors thatcapture either of these senses individually.Continuous patient monitoring can exhibit numerous bene-fits, including a reduction in the number of in-person clinical∗School of Electrical and Computer Engineering, Oregon StateUniversity, 1148 Kelley Engineering Center, Corvallis, OR, 97331 USA†Linus Pauling Institute, Oregon State University, 571 Weniger Hall,Corvallis, OR, 97331 USA‡Department of Electrical and Computer Engineering, Jacobs School ofEngineering, University of California, San Diego, CA 92093 USA§Department of Bioengineering, Jacobs School of Engineering, andInstitute for Neural Computation, University of California, San Diego, CA92093 USAFigure 1. OLAM top view without battery.visits, which can result in both reduced healthcare costs andminimized patient’s inconvenience. Furthermore, the impactof a clinical visit on measurement error can be quite large[3], such that continuous monitoring in a patient’s homesetting can improve the prescribed treatment. For example,sleep diagnosis from a sleep clinic can be expensive, incon-venient, and inaccurate [4] due to the requirement for thepatient to be evaluated only within the hospital setting.The OLAM presented here is specifically designed with non-invasiveness in mind.Other devices exhibit similar function-ality but are less optimal due to large size, uncomfortableattachment at the interface with the body, poor sensorresolution and accuracy, or limited capabilities of the sensormeasurements [5], [6], [7]. OLAM is unique because it issmall, comfortable, and can sense both movement activityas well as ECG/heart rate. This is critical because it allowsfor correlation of movement artifacts with non-contact heart-rate data, and minimization of large movement noises seenin non-contact interfaces [2]. The non-contact sensor pad [2],capable of sensing through a T-Shirt, is adopted and shownin Fig. 1 and Fig. 2 on the next page.II. DEVICE SPECIFICATIONSThe OLAM was optimized to meet several main designgoals: Minimal intrusion into a patient’s daily life, longbattery life, ease of use, and high accuracy. Each of thesemain design goals were addressed throughout the designprocess. The top level design is similar to [8], and is broken2Figure 2. OLAM bottom view of sensor pad (Left). OLAM top view withbattery (Right).Table ISYSTEM SPECIFICATIONSSpecification ValueSize 2”x2”x1/2”Battery 2200mAhPower Consumption (avg) 20mWExpected Battery Life (min) 15 DaysSample Rate 100 HzUnit Cost (1k Quantity) $65Heart Rate Detection 30-250bpmAcceleration Detection ±8gRotation Detection ±1200◦/sinto three basic blocks: data acquisition, data processing, anddata storage. System specifications can be seen in Table I.Data Acquisition, Processing and StorageThe vital-sign data acquired by OLAM is limited by thesensors that are attached, but can be expanded to othersensing modalities (i.e. respiration, EMG) with little designoverhead. In the current setup, the sensors are worn byelderly patients involved in a 6-week, double-blind clinicaltrial, in order to determine the effects of Lipoic Acid(LA) supplementation, recording any subtle changes in hearthealth as well as patient general activity level before/afterLA ingestion.Given the projected patient outcomes as a result of LipoicAcid supplementation [9], the required sensing capabilitiesof this wearable system are limited to: heart rate (singlechannel ECG), and movement activity (three-axis accelerom-eter and two-axis gyroscope).The sample rate was determined by balancing the acqui-sition of accurate data with the minimization of powerconsumed. The most critical concern was the sample rateand the duty cycle required for the system, affecting boththe wakeup/sleep times of the sensors and microcontroller.The timing of both wakeups and sleeps can be seen in Fig. 3.A power cycling of five-second long sampling intervalsevery minute was chosen to provide a reasonable resolutionwithout loss of information. A sample rate of 100Hz wastherefore chosen to resolve any quick movements observedduring the sensing periods.Commercially available, off-the-shelf components were cho-sen in the design of OLAM. A custom application-specificintegrated circuit (ASIC) is planned for future revisions [10]Figure 3. System Power Statesthat will result in lower power operation and smaller size,but for this particular design, “off the shelf” devices enabledquick design iteration. For data acquisition, the followingdevices were used: Texas Instruments (TI) ADS1294 - Afull ECG analog front-end, Analog Devices ADXL345 -Three-axis digital accelerometer, and a STMicroelectronicsLPR530AL - Two-axis gyroscope.These three devices provide a flexible input system that cancover a wide variety of sensor interfaces. Another importantconsideration was the addition of an isolation buffer placedon the input for the capacitive non-contact input. The con-tactless ECG input requires a very high input impedance toappropriately couple the signal into the sensor. Therefore, asimple opamp voltage buffer, as previously used in [11], wasutilized to provide the required input impedence. Due to theuse of this high input impedance, however, coupling noisefrom 60Hz power lines can be substantial. Hence, a single-pole passive low-pass filter was implemented to mitigate this60Hz signal (a DC-blocking filter is already available dueto the capacitive input).A data-capturing window of 5 seconds ensures that even withrelatively large wakeup times, the system can fully wakeupand shutdown without taking too long. This is very importantfor maintaing a low power consumption, as it leads to apower savings of nearly 80%.Data storage on the OLAM is handled with an onboard 2GBmicro-SD card, capable of storing over a month of data andallows straightforward data transfer.Human InterfaceThe interface to the patient is accomplished with a custom-designed elastic band. The device’s heartrate sensor allowsthe OLAM to be completely enclosed in fabric withoutimpacting sensing performance. The device is attached tothe user by a thin band of 87% Nylon 13% Spandexmaterial, much like that used in swimwear, giving it asmooth connection to the body as well as the ability tostretch and form to the user’s movements. The OLAM on apatient can be seen in Fig. 4 on the following page.III. USAGEThe OLAM was designed to require very little interactionfrom the patient. Currently the device is switched on when3Figure 4. OLAM Human Interfacegiven to a patient, and never turned off until they return itup to two weeks later. When returned, the device is chargedover USB.Collecting data requires only removal of the SD card. Thedata is formatted as a comma separated value (CSV) file.Alternative formatting and encryption could be added ifrequired. In our clinical application, IRB approval (insti-tutional review board) ensures that the patients’ privatehealth data is only available to IRB-cleared individualsadministering the clinical study.IV. RESULTS AND DISCUSSIONTesting of the OLAM reveals that the largest problemwith non-contact capacitive sensors is noise. This noise canobscure the heart-rate measurement, and is a function ofhow well the device is physically compressed against thebody. This means that during any patient movement, theaccuracy of the captured data can be difficult to assess. Thesenoise artifacts come from two main sources: decrease of thecoupling capacitance associated with the increased distancebetween body and sensor, and static charge build-up dueto the movement. The static charge buildup is difficult topredict because it depends on the location of multiple exter-nal materials such as clothing. The first problem, however,is highly correlated with movement. Because the OLAMmonitors motion activity and heart-rate data simultaneously,there is the potential for data sensor fusion to filter themotion artifacts [12].While the OLAM prototype is effective in measuring therequired sensor inputs, the size, weight, and cost of the manycomponents could be potentially eliminated by designing acomplete ASIC system-on-a-chip (SoC). As can be seen inTable I on the previous page, major problems of size, weight,and cost are apparant. For example, the ADC, MCU, andseveral other IC’s (amplifiers, filters) could be incorporatedinto an ASIC. Power consumption would also be lowered us-ing a SoC design, reducing the battery requirements leadingto decreased system size, weight, and cost.Table IISIZE, WEIGHT AND COSTMajor Component Size Weight CostBattery 50mm x 50mm x 9.2mm 42g $16.00ADC 16mm x 16mm x 1mm < 2g $11.95MCU 12mm x 12mm x 1mm < 2g $5.60Sensors ~ < 2g $8.07Passive Components ~ < 2g $7.95Other IC ~ < 2g $13.11Figure 5. Increase in 60Hz noise due to disconnect from the bodyHeart Rate and Movement DataConsider the waveform captured by the contactless sensorshown in Fig. 5. We observe a large increase in powerline noise when the sensor is separated far from the bodysurface. When the non-contact sensor is securely compressedagainst the body, multiple heartbeats are observed in a singlesample window, as in Fig. 7 on the next page, allowing foraccurate estimation of the heartrate. In Fig. 6, we show alarge correlation between the heart-rate and accelerometersignals during times of large physical movement.Data capture during movement is very noisy, but alsopresents an opportunity for filtering. As seen in Fig. 6, thereexists a large artifact correlated with the movement, with across correlation calculated as in Equation 1 below:(f ? g) [n] =∞∑m=−∞f [m] g [m+ n] (1)Movement detection is further aided by the two-axes gy-roscope. These two-axes are used to detect rotation, whichcan be helpful in the classification of different activity types[13]. The gyroscope data from a walking trial can be seentogether with the accelerometer data in Fig. 7.V. FUTURE WORKSome of the biggest problems with OLAM are related to theuse of off-the-shelf components. The power consumption,size, and cost can be significantly reduced by using acustom-designed ASIC. The other critical problem is that4Figure 6. Cross Correlation between X-Axis of accelerometer and heartratesensor.Figure 7. Movement data captured while subject was walking (Top).Multiple captured heartbeats (bottom).data processing is currently performed manually. Heartratedata can be automatically processed from the resulting data,using data fusion that can automatically detect if the patientis wearing the monitor or not. Other activities such as sleep-ing can be detected similar to [14]. The final improvementon OLAM would be in the design of bandaid/patch thatattaches the device to the patient, improving patient comfortand minimizing motion artifact noise.REFERENCES[1] J. Kaye, “Putting petabytes to work: Home-based assessment technol-ogy can help elders manage their health,” Aging Today, no. XXXII(1),2011.[2] Y. M. Chi, P. Ng, E. Kang, J. Kang, J. Fang, and G. Cauwenberghs,“Wireless non-contact cardiac and neural monitoring,” in WirelessHealth 2010, WH ’10, (New York, NY, USA), pp. 15–23, ACM,2010.[3] G. Chen and E. Rodriguez-Villegas, “System-level design trade-offs for truly wearable wireless medical devices,” in Engineering inMedicine and Biology Society (EMBC), 2010 Annual InternationalConference of the IEEE, pp. 1441 –1444, sept. 2010.[4] E. de BEURS, A. T. F. BEEKMAN, A. J. L. M. van BALKOM,D. J. H. DEEG, R. van DYCK, and W. van TILBURG, “Consequencesof anxiety in older persons: its effect on disability, well-being and useof health services,” Psychological Medicine, vol. 29, no. 03, pp. 583–593, 1999.[5] L. Grajales and I. Nicolaescu, “Wearable multisensor heart ratemonitor,” in Wearable and Implantable Body Sensor Networks, 2006.BSN 2006. International Workshop on, pp. 4 pp. –157, april 2006.[6] A. Dinh and T. Wang, “Bandage-size non-ecg heart rate monitor usingzigbee wireless link,” in Bioinformatics and Biomedical Technology(ICBBT), 2010 International Conference on, pp. 160 –163, april 2010.[7] D. Higashi, T. Imai, A. Ueno, and O. Miyashita, “A wearable capaci-tive heart-rate monitor for controlling electrically assisted bicycle,” inElectrical Machines and Systems, 2009. ICEMS 2009. InternationalConference on, pp. 1 –6, nov. 2009.[8] U. Anliker, J. Ward, P. Lukowicz, G. Troster, F. Dolveck, M. Baer,F. Keita, E. Schenker, F. Catarsi, L. Coluccini, A. Belardinelli,D. Shklarski, M. Alon, E. Hirt, R. Schmid, and M. Vuskovic, “Amon:a wearable multiparameter medical monitoring and alert system,”Information Technology in Biomedicine, IEEE Transactions on, vol. 8,pp. 415 –427, dec. 2004.[9] T. Hagen, J. Liu, J. Lykkesfeldt, C. Wehr, R. Ingersoll, V. Vinarsky,J. Bartholomew, and B. Ames, “Feeding acetyl-L-carnitine and lipoicacid to old rats significantly improves metabolic function whiledecreasing oxidative stress,” Proceedings of the National Academyof Sciences of the United States of America, vol. 99, no. 4, p. 1870,2002.[10] P. Chaing, “Wearable sensor electronics for monitoring the effectsof aging,” Radio Frequency Integrated Circuits Sympoisum (RFIC),2011.[11] Y. Chi, T.-P. Jung, and G. Cauwenberghs, “Dry-contact and non-contact biopotential electrodes: Methodological review,” BiomedicalEngineering, IEEE Reviews in, vol. 3, pp. 106 –119, 2010.[12] M. Roemer, G. Kacprzynski, and R. Orsagh, “Assessment of data andknowledge fusion strategies for prognostics and health management,”in Aerospace Conference, 2001, IEEE Proceedings., vol. 6, pp. 2979–2988 vol.6, 2001.[13] J. Parkka, M. Ermes, K. Antila, M. van Gils, A. Manttari, andH. Nieminen, “Estimating intensity of physical activity: A comparisonof wearable accelerometer and gyro sensors and 3 sensor locations,”in Engineering in Medicine and Biology Society, 2007. EMBS 2007.,pp. 1511 –1514, aug. 2007.[14] Z. Shinar, A. Baharav, Y. Dagan, and S. Akselrod, “Automatic detec-tion of slow-wave-sleep using heart rate variability,” in Computers inCardiology 2001, pp. 593 –596, 2001.

OLAM: A Wearable, Non-Contact Sensor for Continuous Heart-Rate and Activity Monitoring

https://ir.library.oregonstate.edu/downloads/pg15bg291

OLAM: A Wearable, Non-Contact Sensor for Continuous Heart-Rate and Activity Monitoring

Abstract

Similar works

Full text

Available Versions

ScholarsArchive@OSU