TOWARD INTELLIGENT NURSING ENVIRONMENT BY USINGWEARABLE SENSOR SYSTEM

Noriaki Kuwahara1, Haruo Noma1, Nobuji Tetsutani1, Kiyoshi Kogure1, Norihiro Hagita1,Hiroshi Iseki2
1

Advance Telecommunications Research Institute International, JAPAN2Tokyo Women's Medical University, JAPAN

Abstract- The constant hard work of providing nursing care,including handling emergencies, often causes medicalaccidents in hospitals. We have prototyped a wearable autoevent-recording system for medical nursing in order tocapture the events that cannot be overlooked in analyzing
such accidents. Our system consists of wearable sensors thatrecord the footsteps and the posture tilt of the nurse inaddition to the voice. We conducted experiments, and theresults show that, without disturbing nurses’ work, oursensors can record data and reconstruct nursing histories,
including the important events that cannot be overlooked foranalyzing medical accidents. In this paper, we show theresults of sensor data analysis comparing a nurse’s datataken in AM hours with those in the PM hours in order tostudy the effect of her fatigue on her motion. Also, we discusson the possibility of detecting the nurse’s fatigue by using oursensor system.
Keywords - wearable, nursing, incident, accident

Tilt Sensor for
Measuring Posture

MicrophoneModulator

Non-touching Switchfor Voice Recording

Pedometer for
Measuring Foot StepsIC RecorderFig. 1. Overview of wearable sensor systemusing our sensor system, since nurse fatigue is one of thepossible causes of the medical accidents.

I. INTRODUCTION

II. OVERVIEW OF WEARABLE SENSOR SYSTEM

One of the most urgent issues in hospitals is toconstruct an intelligent nursing environment to prevent
medical accidents, since they damage the reputation of thehospital’s reliability and thus its business success.
Whenever an accident happens in a hospital, the analysisof the root cause depends on such sources as the doctors’
and nurses’ records. Lately, several intelligent
environments have been introduced, where nurses input
bar-code data into a special PDA that is infection-proof [1].
However, carrying and operating the PDA interferes withnursing duties such as lifting patients and carrying meals topatients. The PDA, therefore, is not user-friendly fornurses. In order to provide a user-friendly device fornurses, nurses must be free from carrying the device
during their nursing duties, and their nursing cares should
not be impeded by interacting with the device while
inputting data. In addition, the device must not misssignificant events that might be related to incident oraccident cases, even if nurses fail to input the data of theseevents. In order to meet these requirements, we havedeveloped wearable sensors attached to a nurse rather thanusing a PDA [2]. Our system records the footsteps and theposture tilt of the nurse in addition to the voice data. We
adopt voice recording to obtain more detailed informationon the nursing care, such as patient’s name and names ofother involved medical staff, by using the speech
recognition [3]. A non-touch switch is introduced tochange between recording time and privacy time whilegathering voice data. Consequently, without disturbing
nurses’ work, our sensors can record data and reconstructnursing histories, including the important events that are
typically never recorded on written records [2]. In thispaper, we focus on a sensor data analysis comparing a dayshift nurse’s data taken in AM (the 7:00 to noon period)
with those taken in PM (the noon to 16:00 period) in orderto study the effect of her fatigue on her motion. Then, wediscuss the possibility of detecting the nurse’s fatigue by

The motion of the nurse indicates the specific patternof each type of nursing care, so we assume that it ispossible to identify job units of nursing care by analyzing
the features of the number of footsteps and the posture tiltof the nurse. We also assume that these patterns reflect thenurse’s physical condition such as fatigue. In order tocheck the nurse’s physical condition, it is desirable toutilize biomedical data such as heart rate, blood pressure,etc. However, the sensors used to measure these data
impede the nurse’s motion. In a real nursing environment,
we cannot attach sensors to nurses as extensively as
previous research efforts did [4]. Fig. 1 shows a nurse
wearing our sensors. All sensor data are converted tosound by the modulator in order to record these data in anIC recorder. The IC recorder is able to continuously record
sound for more than five hours, and it is easy for nurses tohandle. Nurses only need to push the REC button before
recording, and when it runs out of storage, they only needto replace it with a new one to continue recording. Ofcourse, it would be advantageous to connect our wearable
sensors via wireless LAN and to process data in real time,
but there are many issues involved in using radio signals ina hospital. Therefore, as the first step in our approach, we
do not use a wireless LAN. The sound data in the IC
recorder is uploaded to a PC. Then, the software separates
these data into the footsteps, the posture tilts and voice
data by FFT.
III. EXPERIMENT / DISCUSSION
We conducted our experiment at Tokyo Women's
Medical University (TWMU) Hospital. The subjects were
five nurses in the general ward and one nurse in the ICU.
They performed their normal duties for a day (from 7:00 to16:00) while wearing our wearable sensors. In order toevaluate the proposed sensor system, we have alreadyreported that by analyzing the feature vectors of the0-7803-7944-6/03/$17.00©2003 IEEE.

footsteps and the posture tilt of the general ward nurses,
their job units could be statistically categorized into at least
four groups [2].
Then, in order to analyze the effect of fatigue from thefeature vectors of the sensor data, we compared the featurevectors in AM and those in PM, because it is quite naturalthat a nurse would get much tired in PM than in AM. We
also confirmed that nurses subjectively felt more fatigue inPM than in AM. Therefore, it might be possible that thisfatigue influences a nurse’s motion. For this analysis, it isnecessary to compare samples of the same job that wereperformed for the same patient by the same nurse in order
to eliminate the effects of job type, patient and nurse. We
used the ICU nurse’s data because, in the TWMU Hospital,
the ICU nurse is usually assigned as the primary nurse to
one or two patients, while the general ward nurse isassigned as the primary nurse to five or six patients.
Consequently, the ICU nurse has more chances to performthe same care for the same patient than the general ward
nurse in a single day. From the ICU nurse’s voice data,
three main types of jobs were identified as shown below,
and the group means of these categories were statisticallydifferent from each other. Fig. 2 shows the results ofprinciple component analysis.
I.
Writing Record
II.
Communication
III.
Taking Care of Patient in bed
There were not enough samples of Type I for comparing
AM and PM samples. Then, we analyzed the Type II andIII samples. Fig. 3 shows the results of principle
component analysis. According to Fig. 3, it is obvious that
the AM and PM data were separated by the 1st principle
component, and their group means were statisticallydifferent. In the 1st principle component, the average of theposture tilt (positive) and the variance of the posture tilt(negative) are dominant. It can be intuitively understoodthat the posture tilt would increase and the frequency ofchanging it would decrease by fatigue. This suggests thatby monitoring the feature vector, the nurse’s fatigue can bedetected. However, the number of footsteps (positive) was
also dominant in the 1st principle component, and we do
not yet have an explanation for the effect of this factor.
IV. CONCLUSIONS
We introduced a wearable sensor system in order tocapture the important events related to medical accidents.
From the results of our experiment in an actual nursing
environment, an ICU nurse’s job units could be classified
into three categories by using the feature vector analysis of
the sensor data. Furthermore, we found that the feature
vectors in the AM hours and those in the PM hours for thesame kind of job were statistically different. We plan to
use this result to detect nurse fatigue, but we need more
samples for further analysis and study. For future work, inorder to establish an intelligent nursing environment where
our system detects possible dangers of the medical
accidents and alert nurses proactively, our sensors will beconnected via a wireless LAN to process sensor data inreal time and return it as feedback to the nurse.
Accordingly, we are discussing the possibility of using awireless LAN in a ward with hospital staff. Also, we areFig. 2. Result of principle component analysis of ICU nurse
comparing types of jobs

Fig. 3. Result of principle component analysis of ICU nurse
comparing AM and PM data

designing a ubiquitous sensor system that cooperates with
our wearable sensors in order to capture the significant
events that might lead to the medical accidents throughouta hospital.
ACKNOWLEDGMENTS
The authors wish to thank Associate Professor Kawaharaof Kyoto University, Dr. Nakamura of ATR SLT, and Dr.
Kuwabara of ATR IRC for their thoughtful advice. Thisresearch was supported in part by the Telecommunications
Advancement Organization of Japan..
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