Wearable Sensing Systems for Healthcare Monitoring

Wearable Sensing Systems for Healthcare Monitoring Ichiro YAMADA and Guillaume LOPEZ School of Engineering, the University of Tokyo, Tokyo, JAPAN Japan Science and Technology Agency, CREST yamada@mech.t.u-tokyo.ac.jp, guillaume@lelab.t.u-tokyo.ac.jp Abstract Since our society is rapidly aging, there exists an urgent need to shift from passive medical care to preventive medicine and health management, to improve individuals quality of life and reduce medical expenses. At the same time, rapid advances in micro-machine and LSI technologies with revolutionary advances in wireless information and communication technology have enabled development of wearable sensing systems for healthcare monitoring in daily life. We have been developing a comprehensive physiological and environmental information processing platform on the basis of wearable sensors for services to counter lifestyle diseases. This paper focuses on wearable physiological sensing and its applications to healthcare. Introduction A. Social Background of Healthcare Monitoring Nowadays, at the beginning of 21 century, developed countries are aging at an unprecedented rate. The world now has 600 million people over 60-years old and about 860 million chronic disease patients. Japanese society is especially aging rapidly: the ratio of population aged over 65-years old was more than 23% in 2010 and is expected to exceed 30% in 2025 [1]. This is accompanied by an increase in both medical expenses and the number of patients with lifestyle diseases. Therefore, there is urgent need to shift from passive medical care to preventive medicine and health management to improve individuals quality of life (QOL) and reduce medical expenses. When physicians are interviewed about the issue of preventive medicine and health management, they say the first thing to grasp is the patient s current situation. For example, as some symptoms such as masked hypertension do not appear at hospital, there is a strong demand for collecting continuous data over a long period in daily life, recording the variation of data, or collecting objective, not subjective, data by medical interviews. Secondly, it is also important to confirm the effectiveness of countermeasures built-up for individual patients, and the effects are expected to be recorded and presented continuously in daily life and then used as positive feedback for future analysis and diagnosis. For example, physicians want to know if their patients are correctly doing therapeutic exercises and medical diets. Thus, there is a rising need for healthcare monitoring that is available anytime and anywhere. Fig. 1 Medical Service Evolution with Micro-Mechatronics B. Micro-Mechatronics Technologies for Wearable Sensing Systems Information and communication technology (ICT) has enabled the introduction of a broad range of applications such as tele-consultation, and tele-surgery to support wellness and independent living. Additionally, ambient sensors embedded in a patient s environment can provide global health information by continuously monitoring and analyzing the patient s home activity. However, such pervasive systems are still limited to a closed environment and do not sense physiological information directly. Recently, wireless communication systems, as seen in WiFi, Bluetooth, Zigbee, Near Field Communication (NFC), and ANT+, have been improved and adopted in mobile phones, smartphones, and healthcare devices [2], [3]. Moreover, advances in micro-mechatronics technologies such as micro-machine and LSI make it possible to develop tiny and light wearable sensors. Non-intrusive wearable sensors with wireless ICT overcome the limitations of hospitalization and emergency care and thereby enable new, wearable health monitoring for individuals (Fig. 1). Wearable Sensing System Outline A. Health Information Processing Platform Since 2007, we have been advancing a five-year project [4] aiming to develop comprehensive physiological and environmental information processing platform (Fig. 2), which will enable continuous monitoring of healthcare data in daily life, so that people can look objectively at their lifestyles by themselves [5]. The platform is based on wearable physiological sensors with wireless networking capabilities that gather collected data. Database technology dedicated to physiological information 978-1-4673-0847-2/12/$31.00 2012 IEEE 2012 Symposium on VLSI Technology Digest of Technical Papers 5

and software infrastructure providing high-level security and detailed privacy control will enable a large amount of gathered data to be accumulated and efficiently shared. Last but not least, presentation technology based on a psychological approach will enable feedback to be adapted to individuals for more efficient services. This paper focuses on wearable physiological sensing and its applications to healthcare. Fig. 3 Time variation patterns of blood pressure The present 24-hour blood pressure monitoring device (ABPM) can measure blood pressure at regular intervals over 24 hours, but is limited by a long measuring interval exceeding 15 minutes and constrained measuring posture. Recently, research on wearable blood pressure monitoring by using the pulse wave velocity (PWV) method has been carried out [6], [7]. However, without entering into the details of either technology, both have the following problems. Performances are only validated at rest due to motion artifact influence. Also, the hand and finger burden is not suitable to daily use. Fig. 2 Healthcare information processing platform B. Wearable Sensors and Analytical Techniques Concerning the research and development of wearable physiological sensing, we are aiming to achieve the following two technologies: (1) unconstrained monitoring using wearable sensors and (2) high-order information extraction of physical and psychological conditions using multivariate analysis of sensor data. Lifestyle diseases are mainly caused by irregular lifestyles, such as lack of exercise, lack of sleep, irregular meals, or stress. Moreover, lifestyle diseases result in heart disease or cerebrovascular disease, which are the causes of 30% of the deaths in Japan. One lifestyle disease is metabolic syndrome, which is a combination of abdominal obesity and two of three other medical disorders: hyperlipidemia, hypertension, and hyperglycemia. Japan has a very high ratio of people with hypertension, so most of the metabolic syndrome patients in Japan suffer from hypertension. We have therefore been focusing on the development of wearable sensing technologies for blood pressure, eating habits, and stress, that is, technologies that would be effective countermeasures against lifestyle diseases. B. Development of a New Wearable Sensing Method We have developed a method using wearable sensors for accurate and non-invasive measurement of blood pressure in daily activities, including during exercise, to obtain new medical knowledge about blood pressure variability. Our method is based on the PWV method, which consists of measuring the pulse transit time (PTT) by using the signals of an electrocardiogram (ECG) sensor on the chest, and a pulse wave sensor on the earlobe. The peak of the R wave approximates the ejection timing of blood by the heart, while the foot point of the pulse wave represents the arrival timing of ejected blood. Here, the estimation formula of systolic blood pressure from PTT was improved by taking into account the non-linear characteristics of blood vessels [8]. Wearable Blood Pressure Sensing A. Issues of Blood Pressure Monitoring Clinical studies reveal that it is important to monitor daily variability patterns and short-term variability patterns of systolic blood pressure as a risk evaluation index for strokes, heart attacks, and other such critical events (Fig. 3). Therefore, the purpose of a wearable blood pressure sensor should be to measure systolic blood pressure continuously in daily life. Fig. 4 Prototype of wearable blood pressure sensor 978-1-4673-0847-2/12/$31.00 2012 IEEE 2012 Symposium on VLSI Technology Digest of Technical Papers 6

The systolic blood pressure estimated using the above new method were compared with the reference blood pressure measured by the physician, confirming the possibility of continuously measuring blood pressure with a less than 10-mmHg error even during physical exercise. Though there are still many issues remaining to improve accuracy, reliability, and usability, the feasibility has been verified. Therefore, the prototype of a wearable blood pressure sensor has been built (Fig. 4), which is composed of ECG sensor and pulse wave sensor at a 1-kHz sampling rate. The terminal is used to control measurement, display raw physiological signals in real-time, and compute instantaneously not only the heart rate but also the systolic blood pressure. Finally, continuous blood pressure records can be uploaded to an online database, together with detailed raw signals. C. Field Evaluation of Wearable Blood Pressure Sensor The developed wearable blood pressure sensor has been evaluated in clinical studies in collaboration with The University of Tokyo Hospital. The clinical study targeted the detection of very-short term blood pressure variations, which is a phenomenon specific to the elderly, together with the continuous monitoring of excessive blood pressure increases during periods of mental stress and physical exercise. The patients wore the sensor while performing several tasks such as changing their posture from lying to standing, walking, going up and down stairs, having a meal, learning a story by heart, and doing mental arithmetic. Figures 5 and 6 represent typical examples of the multiple very short-term blood pressure variations that may occur with elderly patients such as postural hypotension, postprandial hypotension, etc. The systolic blood pressure measured every 5 minutes using ABPM is also plotted as a reference. In these figures, the systolic blood pressure can be verified to correlate well with the ABPM measurement. In this way, by continuously measuring blood pressure, it has become possible for the first time to capture these very-short term blood pressure variations, which are the points to which to pay clinical attention. Eating Habits Monitoring A. Needs for Eating Habits Monitoring A non-invasive and objective method for continuously monitoring eating habits has not been established, that is useful for preventing lifestyle diseases such as metabolic syndrome. Healthcare specialists have identified the following actions as essential in evaluating eating habits. (1) First is mastication counting, since a high eating speed contributes to being overweight. (2) Second is meal-time related activity analysis, such as the regularity of meal times. (3) Last is food texture analysis. Food texture is the quality of food that can be felt by tongue, palate, or teeth, such as crunchy crackers, crispy salads, soft bananas, etc. Food texture not only relates to dental health but also affects weight, since softer food that is easier to break down results in the burning of slightly fewer calories. For mastication counting, there exist the Kamikami sensor and in-ear microphone based sensor. The Kamikami sensor is an accelerometer embedded device to measure up-down motion of the mouth, but it does not perform well and is uncomfortable to wear [9]. The in-ear microphone based sensing technology is more accurate and smaller, but the processing algorithms need to be trained and calibrated for each user [10]. As for food texture analysis, there is not much research. Researchers using the in-ear microphone tried to evaluate food texture by applying principal component analysis (PCA) to FFT-based features, but were unable to evaluate food texture quantitatively [10]. Therefore, we have been developing a non-invasive and objective sensing system that can record eating habits for a long period of time, as well as robust analytical methods, that can be used to accurately monitor eating habits in daily life (Fig. 7). The sensing system is composed of bone-conduction microphones from which internal body sounds are collected. B. Mastication Counting Fig. 5 Example of continuous BP measurement Schellong test and meal test Fig. 6 Example of continuous BP measurement physical exercise and mental stress Chewing sound characteristics have to be considered to increase the accuracy of mastication counting. On the basis of the chewing mechanics, we built a new mastication counting model by introducing the following two original technologies [11]. For the chew feature extraction algorithm, a new method called amplitude differences accumulation (ADA) was proposed, which emphasizes the difference between chews and pauses and is more robust to the low signal amplitudes of soft foods. For the peak detection logic, a rule-based threshold was introduced that takes into account the chewing mechanics relative to time, instead of a conventional threshold relative to signal amplitude, and is more robust to individual chewing differences such as speed, strength, etc. The proposed model was validated and compared with three other existing models. It showed accuracy better than 90% as well as greater robustness to individual and food differences. 978-1-4673-0847-2/12/$31.00 2012 IEEE 2012 Symposium on VLSI Technology Digest of Technical Papers 7

combined with a hidden Markov model (HMM) [12]. In the proposed analytical model, after sound segmentation, wavelet features are extracted and used as input for a dedicated HMM, which is trained to return as output the level of three food textures: hardness, elasticity, and crunchiness. The proposed wavelet features and HMM combined model achieves a high estimation accuracy of more than 90%, with an individual variance of 4% and a food variance of 3% (Fig. 9). Fig.7 Eating habits sensing device and system C. Food Texture Analysis from Chewing Sound To extract useful information about food texture, we adopted wavelet features instead of FFT-based features, since wavelet features can deal with detailed information in both the frequency and time domains. Figure 8 shows the PCA results obtained using wavelet features. It was proved that the first principal component (PC) axis can be used to represent food hardness: the further right, the softer. We can see that a rice cracker is harder than lemon bread, which in turn is harder than a cream puff. From these results, we obtained two key findings: (1) the time trajectory of the chewing process can be visualized, and (2) the first PC value of the first chew is related to food hardness. This method requires calibration for each individual, however, and can only estimate food hardness. Fig. 8 Time trajectory of chewing process (results of PCA using wavelet features) We therefore proposed a new food texture analytical model for detailed food textures estimation using wavelet features Fig. 9 Estimation results of food textures Workplace Stress Monitoring A. Technological Issues in Stress Monitoring Current stress detection methods rely on inquiry sheets or interviews with a medical specialist. However, because stress is so pervasive in our daily activities, there is an inherent need to monitor stress continuously in daily life over an extended period. Previous research related to stress study using wearable physiological sensing can be classified into two categories: one demonstrates the causal relationship between stress and changes in physiological indices [13], and the other attempts to estimate the occurrence of stress on the basis of the observation of changes in physiological indices [14]. To monitor stress in daily life, the following issues have to be addressed. (1) As physiological indices are strongly influenced by individual differences, their values on stress occurrence differ depending on each individual [15]. (2) Depending on the type of stress, different physiological indices react, so that it is difficult to estimate stress in detail from a single physiological index [16]. (3) Stress status output models are often limited to an output of having or not having stress and do not estimate stress status in terms of details, such as the type of stress [17]. From such viewpoints, we focused on estimating three types of workplace stress [18]. (1) Monotonous stress, which is generated when doing repetitive work with little content change for a long time. (2) Nervous stress, when doing work in which mistakes cannot be afforded (pressure to public speech, meeting with hierarchical superiors, etc.). (3) Normal stress, when performing basic work that does not generate extra stress. 978-1-4673-0847-2/12/$31.00 2012 IEEE 2012 Symposium on VLSI Technology Digest of Technical Papers 8

B. Stress-Related Physiological Indices and Database Construction Focusing on autonomic nervous system activity, we did not use an electroencephalogram (EEG) due to its difficulty for real-time monitoring. In this study, we attempted to measure simultaneously ECG, pulse wave, respiration, and finger-skin temperature, which are known to reflect the activity of the autonomic nervous system. From these signals, several physiological indices were extracted: heart rate (HR), variation in RR intervals (RRV), low and high frequencies ratio of RRV (LF/HF), pulse arrival time (PAT), respiratory central frequency (f G ), average temperature of finger skin (T F ), etc. To collect physiological indices for the three types of workplace stress and construct a database, we used the Paced Auditory Serial Addition Test (PASAT), defining a 5-minute PASAT task to stimulate a normal stress reaction (PASAT1), a 60-minute PASAT task for a monotonous stress reaction (PASAT2), and a 5-minute PASAT task combined with reward cutting on mistakes for a nervous stress (PASAT3). The PASAT tasks were performed by about 100 participants, aging from 15 to 47 years old, using self-assessment results to validate the stimulated stress types. C. Workplace Stress Type Estimation We have proposed a new method for stress type estimation applicable with a high-generality [18]. The proposed method consists of an original multi-step logic, as shown in Fig. 10, to perform individual-independent stress type estimation with high accuracy. The first step aims to discriminate a stressed state from a relaxed state, in other words the presence of workplace stress. The second step aims to discriminate normal stress from the other workplace stress, which means the harmful stress. Finally, the third step aims to discriminate the physiological reactions between nervous stress and monotonous stress. Another important point is the selection of the best physiological indices at each step, since a different physiological reaction arises for each stress type. Fig. 10 Procedure of work stress type estimation The proposed method was evaluated by comparing its performance with those of conventional methods that use only one physiological index or do not perform features selection. A detailed result of efficiency analysis proved that introducing physiological indices selection has a great impact on the accuracy of stress presence estimation (87%±3%) and that adopting multi-step logic is essential for improving the accuracy of stress type estimation (63%±5%). In addition, results using 39 participants of various ages and both genders, demonstrated that this method is less susceptible to individual differences. Conclusion Since our society is rapidly aging, there exists an urgent need for wearable healthcare monitoring. In this paper, we presented wearable sensing technologies for blood pressure, eating habits, and workplace stress, including their social backgrounds. Focusing on blood pressure sensing, we will continue to develop wearable physiological sensors and analytical techniques with a high-level completion. We will also continue to develop a comprehensive healthcare information processing platform, integrating the technologies of wearable sensing, database, information sharing, and so on. For future field studies, the development of an information sharing service among medical staff, patients, family members, and so on will be very important. Moreover, the further advances in micro-machine (MEMS/NEMS) and VLSI technologies are expected to make wearable sensors tinier and lighter and consume significantly less energy. References [1] http://www8.cao.go.jp/kourei/whitepaper/index-w.html [2] Bluetooth SIG, Health Device Profile Specification Vol. 1.0, http://www.bluetooth.org/ [3] Continua Health Alliance, Version2010 Design Guidelines, http://www.continuaalliance.org/products/design-guidelines.html [4] JST (Japan Science and Technology Agency)-CREST (Core Research for Evolutional Science & Technology) project on Development of a Physiological and Environmental Information Processing Platform and its Application to the Metabolic Syndrome Measures. http://www.jst.go.jp/kisoken/crest/en/area02/1-01.html [5] M. Shuzo and I. Yamada, Development of Biological & Environmental Information Processing Platform, in proceedings of the 2009 Japan Electrical Engineering Society Technical Meeting, Sapporo, March 2009. (in Japanese) [6] J. Espina, T. Falck, J. Muehlsteff, Y. Jin, M. A. Adan, and X. Aubert, Wearable body sensor network towards continuous cuff-less blood pressure monitoring, Proceedings of the 5th International Summer School and Symposium on Medical Devices and Biosensors (ISSS-MDBS 2008), pp. 28 32, 2008. [7] D. McCombie, A. Reisner, and H. Asada, Motion based adaptive calibration of pulse transit time measurements to arterial blood pressure for an autonomous, wearable blood pressure monitor, Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2008), pp. 989 992, 2008. [8] G. Lopez, M. Shuzo, H. Ushida, K. Hidaka, S. Yanagimoto, Y. Imai, A. Kosaka, J.-J. Delaunay, and I. Yamada, Continuous Blood Pressure Monitoring in Daily Life, Journal of Advanced Mechanical Design, Systems, and Manufacturing, Vol. 4, No. 1, pp. 179-186, 2010 [9] http://www.nittokagaku.com/kamikami/product.html [10] J. Nishimura, and T. Kuroda, Eating habits monitoring using wireless wearable in-ear microphone, In Proceedings of International Symposium on Wireless Pervasive Communications (ISWPC08), IEEE, 2008. 978-1-4673-0847-2/12/$31.00 2012 IEEE 2012 Symposium on VLSI Technology Digest of Technical Papers 9

[11] H. Zhang, G. Lopez, M. Shuzo, J.-J. Delaunay and I. Yamada, Mastication Counting Method Robust to Food Type and Individual, International Conference on Health Informatics (HEALTHINF 2012), pp. 374-377, 2012. [12] H. Zhang, G. Lopez, R. Tao, M. Shuzo, J.-J. Delaunay and I. Yamada, Food Texture Estimation from Chewing Sound Analysis, International Conference on Health Informatics (HEALTHINF 2012), pp. 213-218, 2012. [13] C. Schubert, M. Lambertz, and R. Nelesen, Effects of stress on heart rate complexity - a comparison between short-term and chronic stress, Biological Psychology, vol. 80, no. 3, pp. 325 332, 2009 [14] K. Itao, T. Umeda, G. Lopez, and M. Kinjo, Human recorder system development for sensing the autonomic nervous system, Proceedings of the 7th Annual IEEE Conference on Sensors (Sensors 2008), pp. 423 426, October 2008. [15] S. Miyake, Factors influencing mental workload indexes, Journal of University of Occupational and Environmental Health (J. UOEH), vol. 19, no. 4, pp. 313 325, 1997. [16] S. Miyake, Multivariate workload evaluation combining physiological and subjective measures, International Journal of Psychophysiology, vol. 40, no. 3, pp. 233 238, 2001. [17] A. Sul, J. Shin, C. Lee, Y. Yoon, and J. Principe, Evaluation of stress reactivity and recovery using biosignals and fuzzy theory, Proceedings of the 2nd Joint EMBS-BMES Conference, pp. 32-33, 2002. [18] H. Ide, G. Lopez, M. Shuzo, S. Mitsuyoshi, J.-J. Delaunay, and I. Yamada, Workplace Stress Estimation Method Based on Multivariate Analysis of Physiological Indices, International Conference on Health Informatics (HEALTHINF 2012), pp. 53-60, 2012. 978-1-4673-0847-2/12/$31.00 2012 IEEE 2012 Symposium on VLSI Technology Digest of Technical Papers 10