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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 23, NO. 2, MARCH 2019

Noncontact In-Bed Measurements ofPhysiological and Behavioral Signals Using an
Integrated Fabric-Sheet Sensing Scheme
Mayuko Takano, Student Member, IEEE, and Akinori Ueno

Abstract—Home monitoring requires measuring thephysiological and behavioral signals without impairing a
subject’s everyday life. This paper presents an integrated
and noncontact approach for obtaining simultaneous
physiological and behavioral signals of recumbent humans
in beds using a home-monitoring application. In the proposed approach, a fabric-sheet unified sensing electrode(FUSE) obtains physiological signals by recording theelectrocardiogram (ECG), chest and abdominal respiratory
movements (RMs), and ballistocardiogram (BCG). The
FUSE also detects the behavioral signals of body proximity(BPx) and lateral/supine lying postures. A prototype systemwith FUSE was validated in a short-term experiment and6-h overnight measurements on two different groups
composed of seven lying subjects. The results confirmedthat the approach senses each signal independently andrecords the ECG, RMs, BCG, and BPx signals simultaneously. The mean sensitivities of the R and T waves of
the ECG during sleep were 86.1% and 88.0%, respectively,
whereas those of the chest and abdominal RMs were
90.7% and 90.1%, respectively. Although our prototype
system has room for improvement, the results suggestthat our approach enables the unconstrained, nocturnal
monitoring of the physiological and behavioral signals inrecumbent humans. The at-home monitoring of the physiological and behavioral signals is expected to contributeto cost-effective personalized healthcare in the future.
This noncontact and easy-to-install system for in-bed
measurements can facilitate a new era of home monitoring.
Index Terms—Capacitive electrocardiography (cECG),
respiratory movements, ballistocardiogram (BCG), bodyproximity, lying posture, home monitoring, unobtrusive
measurements, vitals monitoring, fall prevention, pressure
ulcer prevention.

Manuscript received December 1, 2017; revised March 6, 2018; accepted March 30, 2018. Date of publication May 7, 2018; date of current
version March 6, 2019. This work was supported in part by the Strategic
Research Foundation Grant-aided Project for Private Universities from
Ministry of Education, Culture, Sport, Science and Technology, Japan,
2015–2017 (S1512001) and in part by SECOM Science and Technology
Foundation. (Corresponding author: Mayuko Takano.)
M. Takano is with the Master’s Program in Electric and Electronic
Engineering, Tokyo Denki University, Tokyo 120-8551, Japan (e-mail:uenolab_takano@uenolab.jp).
A. Ueno is with the Department of Electrical and Electronic Engineering, Tokyo Denki University, Tokyo 120-8551, Japan (e-mail: ueno@
mail.dendai.ac.jp).
Digital Object Identifier 10.1109/JBHI.2018.2825020, Member, IEEE

I. INTRODUCTION
CCORDING to a 2007 United Nations demographic survey, the proportion of the world’s population aged 60 andabove is expected to grow by up to 21.8% between 2007 and2050 [1]. Given the rapidly aging population [2] along withthe advances in information and communication technologies
and artificial intelligence, the physiological and behavioral signals will be realistically monitored at home in the near future.
Such at-home monitoring would play an important role in bothpreventive daily healthcare and post-hospital medical care [3],[4]. The growing number of patients with chronic cardiorespiratory conditions, such as heart failure [5], atrial fibrillation [6],and sleep apnea [7], gives more urgency to this expectation. The
evidence from clinical trials demonstrates that management programs, including home monitoring, improve clinical outcomes,
particularly for patients hospitalized with heart failure (HF) [8].
The United States Veterans Health Administration introduced alarge-scale (national), enterprise-wide home telehealth programand found that it effectively managed chronic care patients at areasonable cost in both urban and rural settings [9].
Electrocardiograph (ECG) signals are among the most crucial
physiological signals to be monitored because cardiovasculardisease (CVD) is the leading cause of death in the United States[5] and coronary heart disease (CHD) is the leading cause ofdeath worldwide [10]. Abnormalities in the baseline ECG arestrongly associated with subsequent CVD and CHD mortalitiesof all types [11]. In addition to ECG signals, it is important
to monitor the respiratory movements (RMs) because sleepdisordered breathing is associated with acute unfavorable effects
on cardiovascular physiology [7]. Bennet et al. [12] studied therecumbent nocturnal physiological patterns of older adults discharged to their homes after hospitalization for HF. Their findings indicated that respiratory rate (RR) is the most importantrisk-adjusted indicator of readmission for HF [12]. The ballistocardiogram (BCG) is another important physiological signal
because the interval between the R-wave peak of the ECG and
the J-wave peak of the BCG (i.e., the RJ interval) is strongly
correlated with the heart’s pre-ejection period [13], [14], which
is a useful indicator of the heart’s contractility [15] and canbe used to determine left ventricular performance in patientswith CVD [16]. In addition, the BCG might provide the relative
stroke volume of the heart [14], [17] and/or the systolic bloodpressure [18].

A

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TAKANO AND UENO: NONCONTACT IN-BED MEASUREMENTS OF PHYSIOLOGICAL AND BEHAVIORAL SIGNALS USING AN INTEGRATED FABRIC-SHEET619

When monitoring behavioral signals, it is essential to monitor bed-entering and bed-exiting body movements because falls
commonly occur around beds in both hospitals and residential care facilities [19]–[21], especially during attempts to enteror leave the bed [22], [23]. Elderly people worldwide are vulnerable to falls and fall-induced injuries [24], and the burden
and cost of falls are expected to increase as populations age[25]–[27]. A signal that reflects body posture could also preventthe development of pressure ulcers in immobilized patients or
frail elderly patients in home care settings. Pressure ulcers cancause a number of adverse health outcomes such as increased
morbidity and mortality and reduced quality of life. Pressure ulcers also represent a significant financial burden to the National
Health Service [28]–[30]. Moreover, by monitoring body posture signals, caregivers could evaluate changes in the sleeping
postures of patients with obstructive sleep apnea (OSA). Thisis important because OSA events occur more frequently andseverely in the supine posture compared to in other postures[31], [32].
During home monitoring, patients’ physiological and behavioral signals must be measured without disrupting their everyday
lives using ambient and unobtrusive methods. Moreover, for themonitoring equipment to be easily introduced and maintained, it
will likely be necessary to integrate several measurement methods. Based on these considerations, we propose an integrated
noncontact approach for in-bed measurements of physiologicaland behavioral signals, namely: ECG, RM, BCG, body proximity (BPx), and lying posture (LP). The approach is based ona newly devised fabric-sheet unified sensing electrode (FUSE).
The viability of the proposed approach is tested using a prototype measurement device and pilot FUSE in both brief andprolonged laboratory experiments.
II. RELATED WORKS
Many noncontact in-bed measurement methodologies havebeen proposed for monitoring physiological and/or behavioral
signals [33]. Capacitive electrodes [34], [35] are promising devices for biopotential measurements, particularly ECG measurements [36], [37]. Capacitive electrodes made of conductive
fabric and placed under a bed sheet can also measure RMs [38],[39]. Capacitive electrodes can also be used to measure ECG(cECG) simultaneously with LP or BPx [40], [41]. Thus, cECG
and a second physiological or behavioral signal were successfully detected in the above studies; however, the simultaneous
detection of three or more signals has not yet been reported.
When the target application allows the ECG to be substitutedby another cardiomechanical signal (e.g., BCG or seismocardiogram), piezoelectric or acceleration sensors are usually used[42]. These sensors can measure multiple signals in the absenceof ECG signals [43]–[46], and some proposed sensor systems
are in the clinical validation phase. Therefore, if a piezoelectric or acceleration sensor could be developed from the same
conductive fabrics as used in conventional capacitive electrodes
and combined with a capacitive electrode, we could realize alarge-scale, high-integrity tool for measuring physiological andbehavioral signals.

III. MATERIALS AND SYSTEMS
A. Fabric-Sheet Unified Sensing Electrode (FUSE)
The FUSE was designed for simultaneous measurements ofcECG, BPx, RM, and BCG signals. The FUSE is placed underthe bed sheet, beneath the back of the lying subject. To prevent sleep disruption, the FUSE is constructed from thin, soft
materials (conductive fabric and insulating textiles; see Fig. 1).
The laminated structure [Fig. 1(a)] suppresses common-mode
noise and electromagnetic disturbance [47], [48] in the cECG
measurements and mitigates the interference between the cECG
and other measured signals. The inner electrodes (1) and (2) inFig. 1 were introduced only for cECG measurements and arearranged in parallel up and down the subject’s heart. The driven
shields (6) and (7) surrounding each inner electrode are connected to the output terminals of separate bootstrapped voltage
followers. The signal ground layer (5) provides double shielding
to both sets of the inner electrode and a driven shield. Layer (5)functions as a capacitive ground (cGND) when coupled with theconductive trunk or upper body of the lying subject. It also plays
the role of two capacitive sensors in combination with the outerelectrodes (3) and (4). The outer electrodes are placed at theshoulder and lower back of the subject for independent BPx andRM measurements of the chest and abdomen. The capacitive
sensor, including the outer electrode (3), is intended for BCG
measurements. To prevent electrode displacement and overlap
during repeated body movements such as rolling, the electrodes
and shielding structures are unified into a unit on a base sheet
constructed of insulating textile.
B. cECG Measurement
In our proposed layout, the inner electrodes coupled to theupper body through the subject’s clothing are considered as
two capacitive couplings with capacitances C 1 and C 2 , asshown in Fig. 2(a). According to the corresponding connectionin Fig. 1(b), the cECG is passively measured as the difference between the potentials at coupling points 1 and 2 (v cEC G (1) andv cEC G (2) , respectively). To suppress the voltage drops fromv EC G (1) to v cEC G (1) and from v EC G (2) to v cEC G (2) , voltage followers with high input impedance are typically placedat the front ends of the following detection circuit [37], [49].
Here, we installed bootstrapped voltage followers at the front
ends; these voltage followers maintain high input impedance
only to AC signals and shorten the baseline restoration time after body movements such as rolling [39]. The frequency band of
the cECG was set at 0.5–100 Hz, and the amplifier gain was set
to 60 dB.
C. Measurements of BPx, LP, RM, and BCG
The outer electrodes and signal ground layer, which are coupled to the upper body through the subject’s clothing, canbe deemed as three capacitive couplings with capacitances
CBPx(3) , CBPx(4) , and CG , as shown in Fig. 2(b). In the upper panel of Fig. 1(b), series-connected capacitors CBPx(3)and CG are incorporated in an astable multivibrator commonly used in BPxchest , RMchest , and BCGchest detection. The

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 23, NO. 2, MARCH 2019

Fig. 1. The proposed measuring system. (a) Cross-sectional configuration of the FUSE. The top layers (1) and (2) are the electrodes for cECG
measurement, and layers (6) and (7) are driven shields that receive the output of the bootstrapped voltage followers as feedback. The electrodes
are doubly shielded by the third and fifth layers of the circuit ground (5), which also detect the BCG. Additional outer electrodes (3) and (4) detect
the BPx and RM signals combined with the circuit ground (5). (b) Top-view configuration of the FUSE and block diagram of the proposed measuringsystem. The FUSE is placed under the bed sheet with the upper outer electrode (3) positioned beneath the upper edges of both shoulders. The
cECG detection system comprises two bootstrapped voltage followers, an instrumentation amplifier with DC suppression, filters, and amplifiers. The
BPx detection circuit consists of an astable multivibrator, a frequency divider, an F/V converter, and a buffer. The RM and BCG signals are derived
from a common BPx signal through corresponding bandpass filters.

multivibrator oscillates at a specific frequency depending on the
net capacitance Cnet(3) of the series-connected capacitor. When
CBPx(3) << CG in the FUSE, Cnet(3) can be approximated by
CBPx(3) , as shown in (1):
CBPx(3) CG
CBPx(3)=≈ CBPx(3) .
C
CBPx(3) + CG
1 + B P x(3)Cnet(3) =(1)

CGThe oscillatory frequency fosc(3) of the multivibrator depends
on CBPx(3) :fosc(3) =

kosc
kosc
≈
,
R3 Cnet(3)R3 CBPx(3)(2)

where R3 is the resistance (Ω) in Fig. 2(b), and kosc is a constantdetermined by the power-supply voltage and threshold voltagesof the Schmitt inverter integrated circuit (National Semiconductor, LM231) in Fig. 2(b). The output voltage Vout(3) of thefrequency-to-voltage (F/V) converter is proportional to the input
frequency fosc(3) as follows:
Vout(3) = kfv · fosc(3) ≈kfv kosc,
R3 CBPx(3)(3)

where the proportionality factor kfv is determined by the resistance and capacitance in the F/V converter circuit. To calculate
CBPx(3) , we need the permittivity of the coupling ε [F·m], thecoupling area AS(3) [ m2 ], and the coupling distance dS(3) [ m] .
The coupling distance is the sum of three time-varying distancesd(3)BPx , Δd(3)RM , and Δd(3)BCG . Specifically, CBPx(3) is calculated as
CBPx(3) = ε

AS(3)
AS(3)
= ε
,
dS(3)
d(3)BPx + Δd(3)RM + Δd(3)BCG(4)where d(3)BPx is the distance between the upper body andouter electrode (3) determined by the lying or rising posture. The distance changes Δd(3)RM and Δd(3)BCG are causedby respiration-associated chest movements and ballistocardiographic displacement, respectively. Substituting (4) into (3), we
obtainkfv kosc d(3)BPx + Δd(3)RM + Δd(3)BCG·.
Vout(3) ∼
=
R3 ε
AS(3)
(5)
When a subject’s upper body moves up or down (e.g., whenentering or exiting the bed), the change in d(3)BPx dominatesΔd(3)RM and Δd(3)BCG . Under this condition, the output voltage Vout(3) is calculated as
Vout(3) ≈kfv kosc d(3)BPx
·
R3 ε
AS(3)

≡ VBPx chest .

(6)

Note that (6) includes the coupling term AS(3) . When thesubject changes his/her body position to or from the lateral
posture, the change in 1/AS(3) dominates d(3)BPx . Therefore,
VBPx chest reflects not only BPx, but also LP around the chest.
When the subject maintains a steady body posture, bothd(3)BPx and AS(3) can be regarded as constants. The change
in output voltage ΔVout(3) is then sensitive to Δd(3)RM andΔd(3)BCG and can be expressed as follows:
ΔVout(3) ∝kfv kosc Δd(3)RM + Δd(3)BCG
R3 εAS(3)≡ vR M chest + vB C G chest ,(7)

where vRM chest and vBCG chest are the components of ΔVout(3)sensitive to RM and BCG, respectively, at the chest. As vRM chest

TAKANO AND UENO: NONCONTACT IN-BED MEASUREMENTS OF PHYSIOLOGICAL AND BEHAVIORAL SIGNALS USING AN INTEGRATED FABRIC-SHEET621

commercially available cotton bed sheets with a thickness of
0.33 mm.
A. BPx Signals With Postural Changes and TheirClassification
As a fundamental evaluation of the proposed system, we
measured the BPx signals at the chests (BPxchest ) and abdomens (BPxabd ) of seven subjects #A–#G [22–45 years old,
1.67–1.76 m height, 50.0–83.0 kg weight, 18.0–23.3 kg/m2
in body-mass index (BMI)]. The subjects were requested tochange their LP sequentially on cue and to maintain each posture for 20 s. The postural sequence was sitting, supine, leftlateral, supine, right lateral, supine, and sitting. The BPx signals
recorded at each posture were divided into five segments, eachwith a duration of 4 s. The voltages of the BPx signals at the chest(VBPx chest ) and abdomen (VBPx abd ) were averaged over each of
the second, third, and fourth segments. Each chest–abdomenpair of mean voltages was considered as a two-dimensional datum and classified into one of three posture clusters (sitting,supine, or lateral) using a K-means clustering algorithm. The
classification was performed for each subject and for all subjects
together. The sensitivity (PSNS ), accuracy (PACC ) and positive
predictive value (PPPV ) of each cluster were calculated by (8),(9) and (10), respectively:
Fig. 2. Schematic of the capacitive couplings associated with the measurement principle of the proposed approach. The couplings consistof the volume conductor of the lying subject, insulating materials (bedsheet and clothes worn by the subject), and the conductive fabric in the
FUSE. (a) Couplings related to cECG measurement. The heart represents a voltage source in the volume conductor, which generates voltages vECG (1 ) and vECG (2 ) via the couplings. The cECG is the potential difference between vcECG (1 ) and vcECG (2 ) . (b) Couplings involvedin the BPx, RM, and BCG measurements. The series connections of
CBPx (3 ) and CG , and CBPx (4 ) and CG are incorporated in the astable
multivibrator circuits, resulting in square pulses vBPx (3 ) and vBPx (4 ) ,respectively. The frequencies of vBPx (3 ) and vBPx (4 ) depend on the
combined capacitances of CBPx (3 ) and CG , and CBPx (4 ) and CG ,respectively.

and vBCG chest have different frequency components, they canbe derived from their corresponding frequency filters [see theblock diagram in Fig. 1(b)]. The frequency bands of vRM chestand vBCG chest were set to 0.15–0.5 and 0.1–40 Hz, respectively.
By considering the series-connected capacitors CBPx(4) and
CG , we can similarly determine VBPx abd , vRM abd , and vBCG abd ,which are sensitive to BPx and RM, respectively, at the abdomen(BPxabd and RMabd ).
IV. EXPERIMENTAL AND ANALYTICAL METHODSAll experimental procedures were approved by the Human
Life Ethics Committee of Tokyo Denki University. All subjects
provided informed consent prior to participation in our experiments. The subjects were accepted onto the study if they had
no prior medical history of cardiopulmonary or cardiovasculardisease. The measuring system is shown in Fig. 1(b). All signals
obtained by the system were digitized at 1 kHz with 16-bit
resolution, with a view to future precise analysis. The reference
voltage was ± 10 V. All subjects wore commercially available
nightwear made from 0.36-mm-thick cotton and slept on

PSNS =

NTP
× 100,
NTP + NFN

(8)PACC =

NTP + NTN
× 100,
NTP + NTN + NFP + NFN

(9)

NTP
× 100,
NTP + NFP

(10)

and
PPPV =

where N TP , N TN , N FP , and N FN are the numbers of data
correctly classified into the target cluster, correctly classified
into other clusters, incorrectly classified into the target cluster,and falsely classified into other clusters, respectively.
B. Evaluation of RM Signals
We tested whether our system could measure RMs with different RRs in a single 24-year-old subject #H (1.71 m height,
61 kg weight, 20.9 kg/m2 BMI) with three different LPs: supine,right lateral, and left lateral. Reference RM signals at the chest(RMref chest ) and abdomen (RMref abd ) were simultaneously measured by telemetric belt-type transducers (TSD201, BIOPAC
Systems) and amplifiers (RESP100C, BIOPAC Systems). The
transducers were worn on the subject’s chest and abdomen. This
test involved only the detection of BPx and RM [Fig. 1(b)] because the transducer worn on the chest could lead to interferencein the cECG measurement. First, the subject was asked to lie
on the bed in a supine position. To verify the RR, the timing ofbreaths was synchronized with a metronome. The measurement
lasted for 90 s at each of eight fixed RRs ranging from 10 to
30 respirations per minute. With the subject lying on the bed in
the right- and left-lateral postures, the RM signals were tested
in the same manner.

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The RM signals including reference signals were preprocessed by filtering twice through a digital bandpass filter (infinite impulse response filter, f c = 0.15–0.5 Hz). Discrete RM
cycles in the RM signals were automatically detected by software with threshold processing [50]. The RR was calculated foreach measurement by dividing the total duration of RMs in a
10.5-cycle recording by the detected number of RMs in therecording. We then calculated the correlation coefficients for
the RRs determined using our new system and those determined by the commercial system used for the reference signal
measurement for the chest and abdomen in three LPs.
C. Multichannel Simultaneous Measurements in Each
Body Posture
A short test in which the cECG, BPxchest , BPxabd (i.e., RMchest
and RMabd ), and BCG signals were measured was performed
on one 24-year-old subject #I (1.73 m height, 83 kg weight,
23.3 kg/m2 BMI). First, the subject was asked to lie supine on thebed. Second, the subject was assigned the following sequenceof postures: left lateral, supine, right lateral, and supine. All
postures were held for 50 s except for the last supine posture,
which was held for approximately 60 s. Finally, the subject was
asked to arise and sit on the bed. The body movements of theparticipants were verified by video recordings.
D. Overnight Measurements
Next, our proposed system was validated through experimental measurements of seven healthy male subjects #J–#P(20–23 years old, 1.60–1.87 m height, 55.0–78.8 kg weight,
20.9–26.6 kg/m2 BMI) during sleep. Reference Lead III ECG
signals were measured by a commercially available ECG
telemetry system (i.e., BN-RSPEC, BIOPAC Systems) anddisposable electrodes (i.e., F-150S, Nihon Kohden). The
reference RM signal at the abdomen (RMref abd ) was measured
with the same telemetry system and a belt-type transducer(BN-RESP-XDCR, BIOPAC systems). In this experiment, therespiration transducer was worn only on the abdomen to avoid
interference with the cECG measurement. Again, the body
movements were verified by video recordings. Blood oxygen
saturation (SpO2 ) was measured using a commercially available
transducer (TSD123A, BIOPAC Systems) and a calculation
module (OXY100C, BIOPAC Systems). For advanced analysis,a sheet-type sensor of a commercial screening device of sleep
apnea syndrome (Sleep EYE GD700, GAC Co., Ltd., approvedin Japan) was placed under the FUSE. A software supplied withthe device can detect apnea/hypopnea episode and calculate
apnea–hypopnea index (AHI). At the start of the measurements,
volumetric humidity was set to 6– 10 g/m3 . The duration of theexperiment was at least 6 h, although only the data collectedduring the first 6 h were analyzed. The subjects were free
to change body positions as they wished. The RM signals
were preprocessed as described in Section IV-B. To allow
comparison with the conventional system, the above-described
subjects, test protocol, and preprocessing method of RM signals
were set to be the same as described in a previous study [39].
Also to allow comparison with the conventional system [39],the Q wave, R wave, and peak and end of the T wave (Ttop

and Tend , respectively) for the reference ECG and cECG weredetected by beat-by-beat pattern matching using the wavelet
transform [51]. The R–R interval (RRI) was calculated fromthe two consecutive detected R waves. If the RRI of a cECG
signal was within ±3 ms of the RRI of the reference ECG,
the latter R wave of the cECG was deemed correctly detected.
For the reference ECG, the Q–T interval (QTI) was calculated
from the detected Q wave and Tend . Bazett compensation was
performed on the QTIs obtained from the reference and our
proposed system as the QTI is known to depend on RRI [52].
Subsequently, the mean (MN) and standard deviation (SD) of
QTI was computed for the 6 h of analyzed data. For cECG,
QTI was calculated from the Q wave of the reference ECG and
detected Tend of the cECG. If the QTI of the cECG signal was
within MN ± 1.96SD of the reference ECG, the T wave of
the cECG was deemed correctly detected. The RM cycles weredetected using the same software as described in Section IV-B.
If the starting time of a detected RM cycle in our system was in
between that of the reference signal, the RM cycle of our system
was deemed correctly detected. The PSNS , PACC and PPPV of the
ECG and RM signals were then calculated.
We investigated correlations of ECG parameters (RRI andcompensated QTI) and RRs (of RMchest and RMabd ) between
the reference and our new systems during 4th 1-h segment in
the 6-h recording for all subject by using a Bland–Altman plot.
For this analysis, correctly detected data (NTP ) were only used.
RRs were calculated by using reciprocal of the total durationof RMs in a 10-cycle window and by shifting the window in abreath-by-breath manner.
We tested whether any decrease in RRI associated with sleep
apnea or/and hypopnea could be detected in the cECG signals.
Here, we referenced an autocorrelated wave-detection algorithmwith adaptive threshold provided by Hayano et al. [53]–[55].
First, the recorded cECG signal was differentiated. The RRI
time series X(t) were generated by interpolation with a spline
function and resampling at 2 Hz [53]. Next, the RRI time seriessatisfying (11) were detected as candidate dips:
X (t) +Δt2
≤ X (t + Δt), (−5 ≤ Δt ≤ 5) .
49

(11)

After calculating the depths and widths of all the candidate
dips, we selected those with depth-to-width ratios exceeding
0.7 ms/s [54]. In addition, the upper and lower envelopes of the
RRI variations were calculated as the 95th and 5th percentile
points, respectively, within a shifting window with a width of
130 s [54]. Finally, we selected the dips with depths greater than
40% of the envelope range. The same analysis was performedon the reference ECG for comparison.
V. RESULTS
A. Classification of Body Posture
Table I tabulates the classification PSNS , PACC and PPPV foreach subject and for all subjects. The sitting postures and LPs
were clearly classified in all subjects, and the supine and lateral postures were perfectly classified in four subjects. The
classification results of all subjects are shown in Fig. 3. The
central points in clusters 1, 2, and 3 describe the supine po-

TAKANO AND UENO: NONCONTACT IN-BED MEASUREMENTS OF PHYSIOLOGICAL AND BEHAVIORAL SIGNALS USING AN INTEGRATED FABRIC-SHEET623

TABLE ISENSITIVITY (P SNS ) [%], ACCURACY (P ACC ) [%] AND POSITIVE PREDICTIVE VALUE (P PPV ) [%] FOR EACH BODY POSTURE

Fig. 3. Cluster plots of V BPx chest vs. V BPx abd obtained from sevensubjects #A-G. Clusters 1, 2, and 3 correspond to the supine position,right- or left-lateral position, and sitting position, respectively. Crosses
mark the center of gravity of each cluster. Plots with overtyped letters x
indicate incorrectly clustered plots.

sition (VBPx chest = 3.0 V and VBPx abd = 3.0 V), lateral position (VBPx chest = 3.5 V and VBPx abd = 5.0 V), and sitting position (VBPx chest = 6.6 V and VBPx abd = 8.4 V), respectively.
The classification PACC were 88.2% for supine posture, 88.6%
for lateral posture, and 100% for sitting posture. These results
indicate that FUSE can provide BPx signals that clearly indicate
the presence of the trunk of the subject (i.e., volume conductor). However, further improvement is needed to distinguishbetween supine and lateral postures (e.g., by combining the RM
and cECG signals).
B. RM Measurements on the Chest and Abdomen in
Each LP
Fig. 4 shows the correlation between RRs recorded by a respiration transducer and those measured using the proposed systemon the chest and abdomen in the supine, right-lateral, and leftlateral postures. The correlation coefficient for the above three
LPs on the chest were 0.97, 0.97, and 0.99, respectively, whereas
those on the abdomen were 0.99, 0.93, and 0.98, respectively.
In addition, root mean square errors (RMSEs) on the chest andabdomen were 1.60 and 1.99 rpm, respectively. These results
confirm that our system adequately detected RMs on the chest

Fig. 4. Correlation between the respiratory rates (RRs) of subject #H
measured by a commercial belt-type transducer and the proposed device. RRs were calculated from both the (a) chest and (b) abdominal
respiratory movements (RMs). The RM intervals were regulated by andvaried using a metronome.

and abdomen in three different LPs, even without band-typetransducers worn on the body.
C. Simultaneous Multichannel Measurement in Each LP
Fig. 5(a) shows the continuous waveforms of the BPx, cECG,
and RM signals in each body posture during the sequence of postural changes. The results confirm that our system measured all
the above signals in all tested postures. The changes in LP produced noticeable changes in BPx voltage, cECG wave profile,and RM amplitude. Although periods of saturation immediately
following rolling-over motions were observed in the cECG and
RM signals, the baseline signals were restored in 5 and 15 s, respectively. Regarding the cECG signal quality, a commerciallyavailable software (AcqKnowledge4.1, BIOPAC Systems) automatically recognized the P, QRS, and T waves in all LPs, as
shown in Fig. 5(b). The RM shape was less distorted on the abdomen than on the chest, and the phases of RMchest and RMabd
were in the reverse in both the supine and right-lateral postures,as shown in Fig. 5(c).
D. Overnight Measurements
Fig. 6 shows example recordings of cECG and RM signals from the proposed and reference systems in each LP obtained during overnight experiment for subject #J. The cECG
and RM signals were synchronized with the simultaneously
measured reference signals, respectively. Further, the P, QRS,

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Fig. 5. Simultaneous measurements of subject #I by the FUSE during resting with sequential postural changes. (a) Reference images of thesubject lying on the bed and noncontact recordings of upper and lower BPxs, cECG, and RMs of the chest and abdomen. Body postures were
measured over 30 s. (b) Enlarged cECG waveforms and (c) enlarged RM signals of the chest and abdomen corresponding to the dotted rectangularsections in the three body postures shown in (a).

Fig. 6. Recording of cECG/ECG and RM signals from the proposed system and the reference systems during overnight experiment for subject#J. Sections were selected when the subject was in the (a) supine, (b) left lateral, and (c) right lateral postures.

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TABLE IISENSITIVITY (P SNS ) [%], ACCURACY (P ACC ) [%], POSITIVE PREDICTIVE VALUE (P PPV ) [%] AND THE NUMBER OF REFERENCE DATA (N TP + N FN )FOR THE R WAVE AND T WAVE OF CECG AND CHEST AND ABDOMINAL RMS

Fig. 7. Correlation plots of (a) RRI, (b) QTI, (c) RR on the chest and (d) RR on the abdomen between the reference and the proposed systems,
and their Bland-Altman plots of (e) RRI, (f) QTI, (g) RR on the chest and (h) RR on the abdomen (Subject #J). All plots were calculated from a 4th
1-hour segment in the 6-hour recording. Subscript “ref” or “pro” on the each parameter indicates that the parameter is obtained from the “reference”
or “proposed” system.

and T waves in cECG were automatically recognized in all LPs
as with Fig. 5(b) by the same software written in V-C.
The PSNS , PACC and PPPV of each 6-h signal for each of theseven subjects are shown in Table II. The average PSNS , PACC
and PPPV of cECG were 86.1%, 86.1% and 99.9% for the R wave
and 88.0%, 83.8%, and 94.4%, respectively, for the T wave.
The PSNS and PACC for subject #J were below 80%, much lower
than those for the other subjects, because subject #J twisted
and turned frequently in the bed, resulting drastic changes in
the waveform and amplitude of the cECG signal. On the other
hand, the PSNS (PACC, PPPV ) of RMchest and RMabd were 90.7%(81.9, 89.4%), and 90.1% (82.4, 90.4%), respectively. The PPPV
of RMchest and RMabd for subject #O were below 85%, much
lower than those for the other subjects, because the numbers offalsely detected of RMs were higher the other subjects. Since thesubject #O reported nocturnal awakenings, the higher numbersof false detection were considered due to small body movements

which were observed more frequently in his recordings than theother subjects.
Fig. 7 shows correlation plots of RRI, QTI, and RRs between
the reference and proposed systems, and their Bland-Altman
plots for subject #J. Correlation coefficients and double SDsof RRI, QTI and RRs for all subjects are shown in Table III.
Both correlation coefficients of RRI and QTI were more than
0.90 in all subjects, showing high degree of accuracy. Further,
the estimated errors for these intervals of the proposed systemwere within ± 3 and ± 11 ms, respectively. The correlation
coefficients of RRs also showed a high degree of accuracy (>0.90) with the exception of RMchest of subject #L. The both
estimated errors for RRs of our system were within ± 2 rpm.
Fig. 8(a) shows the distinctive waveforms measured during
sleep, including during the periods before and after presumed
apnea and/or hypopnea. In the absence of apnea, hypopnea,
and body movement, the waveforms of the cECG and RM sig-

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TABLE III
CORRELATION COEFFICIENT (R) AND DOUBLE SD FOR RRI, QTI, RR DURING 4TH 1-HOUR SEGMENT IN THE 6-HOUR RECORDING

Fig. 8. Representative overnight measurements of subject #J showing apnea and/or hypopnea episodes. (a) Discriminative recordings of BPx atthe chest, cECG, and RMs of the chest and abdomen measured by the FUSE and simultaneously recorded reference signals of SpO2 , ECG, andRM sensed by commercial measuring devices. cECGHPF is the cECG waveform filtered through a high-pass filter (cutoff frequency = 10 Hz). The
RRIs calculated from cECGHPF and ECGref decreased significantly in the sections enclosed by the dotted rectangle. (b) Time series variation in
SpO2 (upper panel), RRIs (middle panel), and smoothed RRIs (lower panel). The RRIs were calculated from cECGHPF and ECGref at the times of
the SpO2 measurements and then interpolated with a spline function and resampled at 2 Hz. The smoothed RRIs were computed from the RRIs in
the middle panel by a second-order polynomial fitting described in Hayano et al. [54]. Crosses and open circles in the smoothed RRIs were detectedby the dip detection algorithm in Hayano et al. [54]. The sections enclosed by the rectangular dotted line match those in (a).

nals were synchronized with the corresponding reference waveforms. However, around the occurrence of a body movement or
breathing efforts associated with apnea or hypopnea involving
a decrease in SpO2 , the waveform of the RM signal desynchronized from that of the RMref signal. In these cases, time was
required to resume synchrony.
VI. DISCUSSION
A. Examination of Postural Distinction
The ability of our device to distinguish posture was evaluatedin two experiments. Our system correctly classified the sitting
postures of all subjects (Fig. 3 and Table I). The voltage wasmuch higher in the sitting position than in the supine and lateral postures because the body was completely separated fromthe FUSE [see Fig. 5(a)]. On the other hand, the lateral posturein subject #D was misclassified as supine, and the supine andlateral postures were confused in subjects #F and #G. Since theSDs of VBPx abd in the supine and lateral postures of subjects#D (0.27, 0.82 V) and #G (0.86, 1.24 V) were higher than in
the other five subjects (0.08-0.15, 0.04-0.43 V), we consider repeatability of VBPx abd value was low in these subjects. In subject#F, the SDs of VBPx abd values in the supine and lateral postures(0.09, 0.15 V) were normal, but the MNs of VBPx abd values inthese postures (3.02, 3.09 V) were close each other and indistinctive. In the lateral posture, the coupling area AS(3) or AS (4)between the upper body and one of the outer electrode of FUSE
is probably smaller than in the supine position. Because a decrease in AS(3) or AS (4) also decreases the coupling capacitance
CBPx(3) or CBPx(4) [see (4)], the output voltage Vout(3) (i.e.,
VBPx chest ) or Vout(4) (i.e., VBPx abd ) should increase slightly [see(5) and (6)]. However, magnitude relationship of VBPx abd values

TAKANO AND UENO: NONCONTACT IN-BED MEASUREMENTS OF PHYSIOLOGICAL AND BEHAVIORAL SIGNALS USING AN INTEGRATED FABRIC-SHEET627(after two rolling movements) in Fig. 5(a) (not shown). These
results imply that the proposed system is able to measure BCG
signals in the supine position after slight positional changes resulting from repeated rolling. However, such peaks were notobserved in the section corresponding to the lateral position,
likely because the subject’s back separated from the FUSE in
this posture, leaving only the shoulder coupled to the FUSE.
In the lateral position, the upper outer electrode (3) shown in
Fig. 1(b) cannot easily measure BCG, whereas the lower outerelectrode (4) retains the contact pressure between the body andthe FUSE. Therefore, in a future work, we will use the lower
outer electrode (4) to measure BCG in the lateral lying position.
C. Evaluation of Overnight Measurements
Fig. 9. Enlarged example of cECGs and possible BCGs of subject#I measured simultaneously by the FUSE during rest with sequential
postural changes. The waveforms in panels (a) and (b) correspond totime sections x and y in Fig. 5, respectively. The arrows in the lower
parts indicate the possible BCG peaks associated with the R waves of
the cECGs in the upper parts.

between the supine and lateral positions was reversed in somesegments of subjects #F and #G. This can be another reason forpoor classification performance between the supine and lateral
postures in subject #F and #G.
Similar phenomena are observed in Fig. 5(a). In Fig. 5(a),VBPx chest is larger in the second and third supine sequences thanin the right- and left-lateral sequences; this behavior is not observed in the BPxabd signals in this subject. Possible contributing
factor is the coupling of the outer electrode (3) to the brachiumand forearm in the lateral positions. As seen in the reference
image of the right-lateral position in Fig. 5(a), the position of
the subject’s left elbow is closer to the shoulder line than in thesupine positions. This might have increased the coupling area
AS(3) , resulting in a lower VBPx chest in the lateral positions.
Although our system correctly classified the sitting and lying
positions, it should be further improved to distinguish among
the various LPs. In the current model, the coupling capacitancein Fig. 2(b) assumes that CS(3) << CG . In contrast, the realelectrode ratio is 27:1. Therefore, the detection PACC might be
improved by narrowing the additional electrode. Using morphological information and cECG amplitude along with BPx signals
might also be an effective to more accurately classify LPs.
B. Ability to Detect BCG
Fig. 9 shows the cECG and BCG signals that were measured
simultaneously as described in Section IV-C. The wave pattern
in Fig. 9(a) corresponds to section (x) of the initial supine position in Fig. 5(a). The BCG signal shows an apparent peak
between the R and T waves of the cECG signal. Fig. 9(b) shows
the wave pattern corresponding to section (y) of the third supine
position (after four rolling movements). A similar peak in BCG
following each cECG R wave can be confirmed in this wave
pattern. A periodic peak with an amplitude of ∼0.3 mV is also
seen in the section corresponding to the second supine position

As shown in Table II, the PSNS and PACC of the R waves ofcECG were identical for all subjects, and MN ± SD of the PPPV
was 99.9 ± 0.1%. In other words, there was almost no false detection of R waves in the proposed system. On the other hand,
the PPPV of the T wave was 5.5 percentage points lower on average than the R wave. The primary reason for the false detectionof the T waves was the wave-like baseline variation in the rightlateral position, which can be seen in Fig. 5(b). Both the R and T
waves were less accurately measured in subjects #J and #K thanin the other subjects. In subject #J, the PACC was lowest during
the first 2 h of measurement, when the cECG signal graduallychanged, and its amplitude fluctuated between 0.15 and 0.6 V.
In subject #K, cECG detection became difficult when the amplitude diminished after a rolling-over movement. Since the SDs of
PSNS and PACC of the cECG were higher than those of the RMs,
we can presume more individual or posture-dependent varietiesof cECG waveform than those of RMs waveforms. As shownin Fig. 5(a), the cECG signal responded to postural changes inour proposed system. Therefore, cECG was difficult to detectin some sections using the present analysis algorithm becauseof drastic changes in its amplitude and waveform. To properly
detect the cECG signal in difficult situations, the software must
be improved to deal with changing cECG signals. This will be
addressed in a future work.
In addition, one of the major factors that lowered detection
performance of cECG was body movement. In previous work[56], we reported shortening of baseline restoration time ofcECG from signal saturation after body movements by using
driven seat ground (DSG) [57]. Therefor introduction of the
DSG can be beneficial for the proposed system to mitigate influence of the body movements and to improve detection performance of cECG.
In the RM measurements of all subjects, the PACC was
lower than the PSNS (see Table II). The least PPPV (81.3%) was
found on the chest of subject #O, indicating a large number offalse detections. One possible explanation for this discrepancy
is the misclassification of baseline variation after a body
movement as RM. Therefore, one of the next challenge to beaddressed is to reduce influence of the body movements on RM
detection by introducing some new methods such as singular
spectrum analysis [58]. When comparing PACC between thechest and abdomen, the chest PACC was higher or lower than628

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Fig. 10. Comparison of (a) sensitivity (P SNS ) and (b) accuracy (P ACC ) of the chest and abdominal RM signals between the conventional andproposed systems. The results of the conventional system are taken from [39]. The same subjects participated in both studies.

the abdominal PACC depending on the individual subject.
This can reflect the different respiratory strategies of different
individuals (i.e., chest respiration vs. abdominal respiration).
Thus, it would be advantageous for the system to independently
measure both chest and abdominal RMs.
Fig. 10 compares the average RM PSNS and PACC of the
proposed system and a conventional system [39]. All subjects
participating in this test also participated in the overnight measurements performed in a previous study [39] and were assigned
the same ID orders. The RM signals at the chest and abdomen
were derived from the cECG signals in the conventional system, whereas they were derived from the BPx signals in the
proposed system. The average PSNS on the chest and abdomen
were approximately 14 and 10 percentage points higher, respectively, in the proposed system than in the conventional system[Fig. 10(a)]. The greater PSNS in the proposed system was significant (p < 0.05 for both RMchest and RMabd , paired t-test). The
RM PACC of the proposed system were approximately 11 and 8
percentage points higher on the chest and abdomen, respectively,
compared to those of the conventional system [Fig. 10(b)]. In the
RMchest measurements, both the PSNS and PACC were higher for
all subjects in the proposed system than in the conventional system. These results confirm the superiority of the proposed system over the conventional system in terms of RM measurement.
D. Detection of Apnea and/or Hypopnea
In a number of sections in the overnight test of subject #J,
SpO2 decreased by more than 3% [see Fig. 8(a)]. Since the result
obtained from the apnea screening device showed 9.8 of AHI for
this subject and detected multiple episodes of apnea/hypopnea
in the segment shown in Fig. 8, the decreases in SpO2 were assumed to be caused by apnea and/or hypopnea. Video recordingsin this segment revealed many body movements (e.g., rollingover movements and neck/hand movements) after the decreasein SpO2 . These body movements were measurable by the BPx
signal in our proposed system.
As shown in Fig. 8(a), the baseline cECG fluctuated aroundthe periods of decreased SpO2 ; as a result, it was not clear
whether the R wave was synchronized with the reference ECG

wave (i.e., ECGref ). In contrast, in the high-pass-filtered cECG
signal (i.e., cECGHPF ), the R waves were clearly visible, and RRI
was easily calculated. Fig. 8(b) shows the time series of SpO2 ,RRI, and smoothed RRI during the presumed apnea and/or hypopnea episodes. The dotted rectangular sections (i) and (ii) in
Fig. 8(b) correspond to those in Fig. 8(a). The time series of
RRI in the cECG (RRIcECG ) almost matches that in the ECGref[RRIref ; see the middle panel of Fig. 8(b)]. In Fig. 8(b), recurring
dips in both RRIcECG and RRIref precede repetitive decreases in
SpO2 . This precedence can stem from a long delay of SpO2 calculation algorithm (at least 8 s in the used module OXY100C).
Using the automatic detection algorithm proposed by Hayano
et al. [54], a total of 12 dips in RRI were detected in the 450-s
segment of smoothed RRIref shown in Fig. 8(b) (see the bottom panel). In contrast, 11 of the 12 dips were detected in thesmoothed RRIcECG at approximately the same times. Hayano
et al. proposed an additional algorithm to compute the index
of cyclic variation of heart rate (CVHRI) from the number ofthe dips and the time in bed [53]–[55]; they also proposed that
CVHRI can be used in place of the apnea–hypopnea index toindicate the severity of sleep apnea.
Before the reduction in SpO2 , RMchest and RMabd were oppositely phased in Fig. 8(a). A preliminary test confirmed thatwhen this subject ceased breathing, the amplitudes of RMchest
and RMabd approached zero. In OSA, oppositely phased chest
and abdominal movements are known to persist while the airflow through the airway is blocked [59]. Therefore, we concluded that our proposed system captured the opposite phaseof breathing effort caused by obstructive apnea and/or hypopnea in Fig. 8(a). After the reduction in SpO2 , the amplitudes of
RMchest and RMabd became large and/or unstable, and the RMref
behaved similarly. These behaviors may be explained by the
body movements after effortful breathing. Because of the highsensitivity of the proposed system, the variation in amplitude
was larger than RMref .
The above results suggest that apnea and/or hypopnea
episodes can be accurately detected by simultaneously measuring cECG, two types of RM (at the chest and abdomen),postural changes, body movements (e.g., rolling movements),

TAKANO AND UENO: NONCONTACT IN-BED MEASUREMENTS OF PHYSIOLOGICAL AND BEHAVIORAL SIGNALS USING AN INTEGRATED FABRIC-SHEET629

and SpO2 . Since our proposed system simultaneously measures
all the above parameters except SpO2 without the attachment oftransducers on the body in combination with a pulse oximeter,
the proposed system might be applied in the detection of sleep
apnea and/or hypopnea.
VII. CONCLUSION AND FUTURE WORKS
This study proposed a noncontact system that simultaneously
measures multiple physiological and behavioral signals in recumbent humans. Measurements are performed by the FUSE, aflexible sensor system that can be placed under the bedclothes.
The performance of the proposed system was tested in both
short-term and overnight experiments. The system accurately
classified the sitting postures and LPs and measured the cECG
and RM signals in the supine and right-/left-lateral postures.
cECG, two kinds of BPx, and RM signals were measured simultaneously. These signals are expected to be simultaneously
measured with BCG in the supine posture. In the overnight experiment, the proposed system achieved improvements of 10–14
percentage points and 8–11 percentage points in average PSNS
and PACC of the RM signals, respectively, compared to the conventional system. Therefore, the proposed approach can be applied in to unobtrusively monitor physiological and behavioral
signals in recumbent humans at night. In the cECG measurements, the waveform of the cECG responded to the posture of
the sleeping subject. However, the PSNS and PACC of the cECG
measurements during sleep were below 90%. To improve uponthese results, the analysis algorithm must be improved to accommodate cECG variation and tested on more participants infuture studies.
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