Wearable Sensing Systems for Healthcare Monitoring

Prévia do material em texto

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/258839621
Wearable Sensing Systems for Healthcare Monitoring
Article  in  Digest of Technical Papers - Symposium on VLSI Technology · June 2012
DOI: 10.1109/VLSIT.2012.6242435
CITATIONS
6
READS
1,958
2 authors:
Some of the authors of this publication are also working on these related projects:
Vibration Problem and Control Technology in Information Processing Equipment View project
Mental Health Monitoring View project
Ichiro Yamada
The University of Tokyo
230 PUBLICATIONS   1,194 CITATIONS   
SEE PROFILE
Guillaume Florian Lopez
Aoyama Gakuin University
132 PUBLICATIONS   251 CITATIONS   
SEE PROFILE
All content following this page was uploaded by Ichiro Yamada on 27 May 2014.
The user has requested enhancement of the downloaded file.
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. 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. 
 
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. 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, butis 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. 
 
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]. 
 
 
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. 
Fig. 5 Example of continuous BP measurement 
᧤Schellong test and meal test᧥ 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 6 Example of continuous BP measurement 
᧤physical exercise and mental stress᧥ 
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 
 
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
 
 
 
 
 
 
 
 
 
 
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 foodhardness. 
 
 
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 
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. 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 (fG), average temperature of finger skin (TF), 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
View publication statsView publication stats
	Select a link below
	Return to Proceedings
	Return to Main Menu