Sage Journals: Discover world-class research

Abstract

In this study, we proposed an intelligent health monitoring system based on smart clothing. The system consisted of smart clothing and sensing component, care institution control platform, and mobile device. The smart clothing is a wearable device for electrocardiography signal collection and heart rate monitoring. The system integrated our proposed fast empirical mode decomposition algorithm for electrocardiography denoising and hidden Markov model–based algorithm for fall detection. Eight kinds of services were provided by the system, including surveillance of signs of life, tracking of physiological functions, monitoring of the activity field, anti-lost, fall detection, emergency call for help, device wearing detection, and device low battery warning. The performance of fast empirical mode decomposition and hidden Markov model were evaluated by experiment I (fast empirical mode decomposition evaluation) and experiment II (fall detection), respectively. The accuracy and sensitivity of R-peak detection using fast empirical mode decomposition were 96.46% and 98.75%, respectively. The accuracy, sensitivity, and specificity of fall detection using hidden Markov model were 97.92%, 90.00%, and 99.50%, respectively. The system was evaluated in an elderly long-term care institution in Taiwan. The results of the satisfaction survey showed that both the caregivers and the elders are willing to use the proposed intelligent health monitoring system. The proposed system may be used for long-term health monitoring.

Keywords

Intelligent health monitoring system smart clothing empirical mode decomposition hidden Markov model electrocardiography

Introduction

According to the World Health Organization,¹ the aging population will rise to nearly 25% of the global population in 2050, and this number can even reach 33% for the developed countries. The issues of elderly health care are becoming increasingly important, because more and more health problems occur with age, including heart disease, physical decline, and physical resilience decline. Therefore, developing intelligent elderly health care techniques is of vital importance.

The fast growing of wearable devices has brought their application in intelligent care, including smart bands^2,3 and smart clothing.^4–6 The wireless transmission modes include radio frequency identification device (RFID),^7,8 Bluetooth,⁹ and Bluetooth low energy (BLE).^10,11 Great progress has been made in the field of intelligent care.^12–21 Yao et al.¹⁷ developed a low-cost interrogation system that was able to dynamically interrogate the antenna sensor and wirelessly transmit the acquired data to a smart device. Sundaravadivel et al.¹⁸ introduced a piezo-electric-based accelerometer sensor design which helped in tracking the physical activities of family and friends. With the three-dimensional (3D) printing technique, Zhang and Amft¹⁹ proposed a personal fitted regular-look smart eyeglasses frames equipped with bilateral electromyography recording to monitor the activity of temporalis muscles, so as to monitor chewing and eating. Daher et al.²⁰ developed smart tiles–based elder tracking and fall detection system, which used pressure sensors and accelerometers hidden under the smart tiles. Yao et al.²¹ presented a compact antenna sensor interrogator for pressure sensing; they developed a frequency-modulated continuous wave generator to detect the resonant frequency of antenna sensors, which encoded the pressure information. However, an intelligent health monitoring system based on smart clothing, which provides integrated services such as cardiac function tracking and fall detection, is still lacking.

In this study, we designed and developed a smart clothing–based intelligent health monitoring system, which was composed of smart clothing and sensing component, care institution control platform, and mobile device. The smart clothing was used for electrocardiography (ECG) signal collection and heart rate monitoring. BLE was used for wireless data transmission.

The intelligent health monitoring system provided eight kinds of services for the elders, including surveillance of signs of life, tracking of physiological functions, monitoring of the activity field, anti-lost, fall detection, emergency call for help, device wearing detection, and device low battery warning. The system integrated our proposed Fast-EMD (empirical mode decomposition) algorithm for ECG denoising and HMM (hidden Markov model)-based algorithm for fall detection. Experiments were designed to evaluate the two proposed algorithms. Also, the proposed system was applied and evaluated in a real scenario of elderly health care.

This article is organized as follows. In section “Materials and methods,” methods are described. Experiments and results are presented in section “Experiments and results.” In section “Discussion and conclusion,” discussion and conclusions are given.

Materials and methods

System design

The architecture of the proposed intelligent health monitoring system is illustrated in Figure 1. The system was mainly composed of three parts: (1) smart clothing and sensing component, responsible for receiving data; (2) care institution control platform, responsible for health data analysis and abnormality alert; and (3) mobile device, responsible for displaying of data and warning of abnormal events, and for providing mobile services. The smart clothing collected ECG signals through four electrode patches and then transferred the analog ECG signals to the sensing component through conductive fiber. The sensing component converted the analog ECG signals into digital signals. The G-Sensor of the sensing component was used for collecting the gravitational acceleration (GC) data. The microprocessor unit (MPU) of the sensing component analyzed digital ECG signals and postures (GC) in real time using specific algorithms. The ECG and posture data were then transmitted through BLE, which were received by the BLE-to-WiFi receiver in the environment. The BLE-to-WiFi receiver transmitted the received data to the back-end management platform for analysis, where the data were finally converted into meaningful health data. The health data were presented in Web mode to the mobile device of the nurse station or the caregiver.

Figure 1.

Architecture of the proposed intelligent health monitoring system.

The architecture of smart clothing and sensing component is shown in Figure 2. The design details of the smart clothing are illustrated in Figure 3. The smart clothing was made of conductive fiber. The four electrode patches on the smart clothing were used for collecting analog ECG signals, which were sent to the sensing component. The analog-to-digital converter (ADC) of the sensing component converted the analog ECG signals into digital signals with a sampling frequency of 250 Hz. The digital ECG signals were analyzed by the MPU of the sensing component to obtain the health data, which were sent as broadcast packets in a frequency of 1 Hz through BLE.

Figure 2.

Smart clothing and sensing component.

Figure 3.

Design details of the smart clothing.

The proposed intelligent health monitoring system provided the following eight kinds of services (Table 1):

Surveillance of signs of life. The heart rate of the elderly is detected at any time, and alert is triggered when the heart rate is above 140 Hz or lower than 50 Hz.

Tracking of physiological functions. Using the collected data of step number, monthly and daily step number variation is analyzed. For a specific physical state, it is determined whether the amount of exercise corresponding to the physical state is achieved.

Monitoring of the activity field. The position of the elderly in the care institution is recorded regularly and analyzed to track whether the position is varied or whether the elderly stays indoors.

Anti-lost. It is detected by indoor positioning whether the elderly gets close to the alert area defined by the care institution. The system is capable of detecting up to 10 m.

Fall detection. Falls are detected at any time. On detecting a suspected fall incident, an emergency call for help packet will be sent immediately.

Emergency call for help. The sensing component has an emergency call for help button, which can be used for real-time call for help when the body is found to be abnormal.

Device wearing detection. If there is no sensing component within the system architecture, the required service cannot be reached, so it is important to detect whether the elderly wears the sensing component.

Device low battery warning. For the same reason as (7), it is necessary to detect whether the sensing component is in low battery.

Table 1.

Services provided by the proposed intelligent health monitoring system.

Service	Content	Warning threshold
Surveillance of signs of life	Monitoring of the heart rate of bedridden elders	<50 Hz or >140 Hz
Tracking of physiological functions	Daily/monthly activity, total activity time, times of getting out of bed	1. Does the activity time during the day reach the set standard?2. Is the fall-sleep time and sleep time normal?
Monitoring of the activity field	Recording of activity locations and ranges	Reminder for no activity in more than 1 h
Anti-lost	To remind all the staff when specific elders enter into the alert area	Set according to different fields
Fall detection	Reminder of getting out of bed at night, warning of staying too long in the toilet during the day or the night, warning of falling down	1. More than 15 min in the toilet2. Warning of getting out of bed at night3. Immediate warning of falling down
Emergency call for help	To immediately learn about the location of the emergency	Notified by the elders themselves
Device wearing detection	Abnormal wearing of the device	Whether the smart clothing and the sensor are properly connected
Device low battery warning	Monitoring of device battery	Battery lower than 20%

ECG signal denoising and heart rate calculation

ECG signals are strongly affected by movement artifacts, which must therefore be effectively removed.^22–24 To this purpose, we used empirical mode decomposition (EMD)²⁵ to filter noise. Previous studies have demonstrated that EMD can reduce motion artifact and baseline wander of ECG signals.^22–24 With EMD, the ECG signal, x(t), was decomposed into N intrinsic mode functions (IMFs) and residual, r(t)

$x (t) = \sum_{j = 1}^{N} IM F_{j} (t) + r_{N} (t)$ (1)

The pseudo-code of the EMD algorithm is as follows:

j ← 1 (jth IMF)

r_j₋₁(t) ←x(t) // residual

Extract the jth IMF

3.1. $h_{j, i - 1} (t) \leftarrow r_{j - 1} (t), i \leftarrow 1$ ; // i: number of iterations;

3.2. Extract local maxima and minima of $h_{j, i - 1} (t)$ ;

3.3. Compute upper envelope $U_{j, i - 1} (t)$ and lower envelope $L_{j, i - 1} (t)$ by interpolating local maxima and minima of $h_{j, i - 1} (t)$ , respectively;

3.4. Compute the average of $U_{j, i - 1} (t)$ and $L_{j, i - 1} (t)$ :

$μ_{j, i - 1} (t) \leftarrow (U_{j, i - 1} (t) + L_{j, i - 1} (t)) / 2$ ;

3.5. Update: $h_{j, i} (t) \leftarrow h_{j, i - 1} (t) - μ_{j, i - 1} (t), i \leftarrow i + 1$ ;

3.6. Calculate stopping criterion: $SD (i) = \sum_{t = 0}^{t} \frac{{| h_{j, i - 1} (t) - h_{j, i} (t) |}^{2}}{{(h_{j, i - 1} (t))}^{2}}$ ;

3.7. Decision: repeat (3.2) to (3.6) until $SD (i) < ε$ and then put $IM F_{j} (t) \leftarrow h_{j, i} (t)$ ; // jth IMF

Update residual: $r_{j} (t) \leftarrow r_{j - 1} (t) - IM F_{j} (t)$ ;

Repeat (3) with $j \leftarrow j + 1$ ;

Stop when the number of extrema in $r_{j} (t) \leq 2$ .

In this study, we improved the EMD algorithm to speed up the algorithm for wearable devices. First, calculation of SD in (3.6) was removed and the stopping criterion $SD (i) < ε$ in (3.7) was replaced with $i < iter$ , where iter was the iteration threshold set according to different states of motion. In this work, iter was experimentally set at 14. Second, the stopping criterion in (6) was replaced with the number of extrema in $r_{j} (t) \leq 2$ and $j \geq order$ , where order was a preset iteration threshold for j. In this work, order was experimentally set at 12. The improved EMD algorithm is called “Fast-EMD.” The flow chart of Fast-EMD is shown in Figure 4.

Figure 4.

Flow chart of the proposed Fast-EMD algorithm.

With Fast-EMD, the denoised ECG signal, y(t), was obtained by summing up the 1st, 2nd, …, Kth IMFs, K ≤ N

$y (t) = \sum_{j = 1}^{K} IM F_{j} (t)$ (2)

Using the denoised ECG signal y(t), a novel QRS complex waveform (Figure 5) morphology analysis algorithm called MWqrs proposed in our previous work⁶ was employed to differentiate between QRS complexes and artifacts. Three feature points were calculated after the algorithm detected a possible QRS complex. Heart rate was calculated by

$HR = \frac{60}{t_{int}}$ (3)

where the RR interval, t_int, was the time between two adjacent R-peaks of the denoised ECG signal y(t), as shown in Figure 5.

Figure 5.

Illustration of QRS complexes and RR interval of ECG signals.

Fall detection

In this study, a fall detection method based on acceleration data and HMM proposed by our group was employed.²⁶ For completeness, the method is described in brief as follows. Accelerations along x, y, and z axes are denoted as a_x, a_y, and a_z, respectively. The resultant acceleration norm is $A = \sqrt{a_{x}^{2} + a_{y}^{2} + a_{z}^{2}}$ . The fall state will be reflected in the change of body acceleration. We can classify four distinct patterns of the resultant acceleration A as shown in Figure 6:

Balanced state. The amplitude of the resultant acceleration curve is approximately 1 gravitational (g) acceleration.

Imbalanced state. The acceleration is approximately 0g when the body loses balance in a short period of time.

Falling state. There are multiple peaks once the body collides with ground. The peak of the resultant acceleration can exceed 4g.

Normal state. A person cannot stand up immediately after a fall occurs and the acceleration is almost 1g again.

Figure 6.

Four states of a fall.

The different states of resultant acceleration are collected to build an HMM. We used the Baum–Welch (B-W) algorithm^27,28 to train the HMM, which involved the following three steps:

Intercept the acceleration sampling data of about 0.5 s with a time window to obtain a data set {a_i}, i = 1, 2, …, 100. The data set {a_i} is divided into 10 data units, and the average value of the sampled data in each data unit was calculated to obtain the time sequence {o_i}, i = 1, 2, …, 10.

In order to distinguish the different grades of the motion state, the variation range of the acceleration sampling data is divided into several sections, and the feature values of each section are defined to obtain the observing feature sequence {O_i}, i = 1, 2, …, 10. The transformation from {o_i} to {O_i} is as follows

$O_{i} = {\begin{matrix} 1 if 0 \leq o_{i} \leq 0.4 g \\ 2 if 0.4 < o_{i} \leq 0.7 g \\ 3 if 0.7 < o_{i} \leq 1 g \\ 4 if 1 < o_{i} < 2 g \\ 5 if 2 g < o_{i} \end{matrix}, i = 1, 2, . . ., 10$ (4)

The classical B-W algorithm is used for training the HMM. The major idea of the B-W algorithm is to update the probability weight of the state by recursive iteration, so that the model parameters can better explain the training sequence. We set the threshold of probability P at 51.4%. The probability threshold acts as the criterion to detect the probability of a fall. The flow chart of detecting falls is shown in Figure 7.

Figure 7.

Flow chart of the proposed hidden Markov model (HMM)-based fall detection method.

Experiments and results

Two groups of experiments were performed, experiment I: Fast-EMD evaluation and experiment II: fall detection. Ten young college students (male) were recruited (aged between 22 and 24, height ranging from 165 to 178 cm, and weight between 51 and 76 kg) to participate in the two groups of experiments. Finally, the proposed intelligent health monitoring system was applied and evaluated in a real scenario.

Experiment I: Fast-EMD evaluation

The ECG data of the participants were collected as they exercised for 5 min. They exercised at different speeds such as 0, 1, 2, and 3 km/h. Each condition was repeated twice. We compared the difference using the data with and without Fast-EMD. The accuracy and sensitivity of R-peak detection with Fast-EMD are shown in Tables 2 and 3. From Tables 2 and 3, it can be found that for speed ≤2 km/h, using K = 11 produced the best performance for R-peak detection; and for speed = 3 km/h, using K = 10 did. If we cannot predict the speed, using K = 11 will generally produce the best performance, with the overall accuracy and sensitivity of R-peak detection being 96.46% and 98.75%, respectively.

Table 2.

Accuracy of R-peak detection with the proposed fast empirical mode decomposition (Fast-EMD) method.

K	0 km/h	1 km/h	2 km/h	3 km/h
1	22.38%	28.96%	23.81%	28.13%
2	94.87%	95.67%	92.49%	89.71%
3	97.42%	97.27%	96.22%	93.59%
4	97.70%	97.26%	96.53%	93.39%
5	97.76%	97.81%	96.33%	93.08%
6	97.71%	97.86%	96.67%	93.33%
7	97.81%	97.61%	96.40%	92.98%
8	97.85%	97.68%	96.49%	92.54%
9	97.76%	97.78%	96.44%	92.61%
10	97.73%	97.68%	96.77%	92.82%
11	97.95%	97.80%	96.77%	92.52%
12	97.66%	97.53%	96.34%	92.24%
13	97.78%	97.33%	96.21%	92.48%
14	97.71%	95.84%	95.41%	91.12%

Table 3.

Sensitivity of R-peak detection with the proposed fast empirical mode decomposition (Fast-EMD) method.

K	0 km/h	1 km/h	2 km/h	3 km/h
1	77.85%	82.84%	78.94%	81.32%
2	99.41%	99.63%	98.68%	98.19%
3	99.21%	99.45%	98.71%	97.90%
4	99.41%	99.45%	98.76%	97.54%
5	99.42%	99.67%	98.56%	97.26%
6	99.39%	99.67%	98.83%	97.34%
7	99.44%	99.56%	98.66%	97.09%
8	99.47%	99.58%	98.76%	96.65%
9	99.41%	99.62%	98.71%	96.87%
10	99.39%	99.58%	98.87%	97.00%
11	99.55%	99.56%	98.91%	96.87%
12	99.34%	99.47%	98.64%	96.63%
13	99.44%	99.47%	98.68%	96.69%
14	99.39%	98.70%	98.22%	95.98%

Experiment II: fall detection

We collected 150 datasets pertaining to normal activities including walking, jogging, sitting, standing, and climbing downstairs, and 150 sets of falling data including falling forward, falling backward, falling to the side, falling when walking, and falling when jogging. Cross validation was used. The HMM was built using 30 sets of fall data selected randomly. We built four HMMs using 120 sets. The remaining 30 falling data and 150 normal activities’ data were then used as testing data. The performance of the HMM-based fall detection method is shown in Table 4. The average accuracy, sensitivity, and specificity of fall detection using the HMM were 97.92%, 90.00%, and 99.50%, respectively.

Table 4.

Performance of the proposed hidden Markov model (HMM)-based fall detection method.

#	Accuracy	Sensitivity	Specificity
1	97.78%	93.33%	98.67%
2	98.89%	93.33%	100.00%
3	98.33%	90.00%	100.00%
4	96.67%	83.33%	99.33%
Average	97.92%	90.00%	99.50%

Application in real scenario

The proposed intelligent health monitoring system has been applied in an elderly long-term care institution, that is, Yi-De Elderly Long-term Care Center, Min-Sheng Healthcare, Taoyuan, Taiwan. The elderly care institution has a room area of 9917 m². The capacity is 200 people. A floor of the institution was used for experiment scenario, which was shaped like $Π$ . There were 50 subjects from 10 rooms, with 5 subjects from each room. Each subject wore a set of smart clothing with sensing components.

The nurse station at the floor was equipped with a central control center. The main functions of the control center included (1) adding, modifying, and deleting personal basic data, and setting the BLE-to-WiFi receiver; and (2) monitoring the position and gait information of the subjects, and altering when an emergency event occurred; the caregiver could check at any time on the Web through the mobile device. In the environment, we used power over Ethernet (POE)-powered BLE-to-WiFi receivers. Each room was equipped with one BLE-to-WiFi receiver. There were also a number of BLE-to-WiFi receivers equipped at walkways, halls, elevators, and escape exits.

Through the sensing component, the information of subjects’ positions and gaits and component abnormity can be obtained. When a subject has an emergency, such as falling, entering into the alert area (elevator, escape exit, etc.) and pressing the emergency button on the sensing component, the caregiver can receive the alert notification and conduct real-time treatment through the central control center at the nurse station or through the mobile device.

The proposed intelligent health monitoring system collected and recorded the subjects’ data of daily events, including the times of staying in situ for over 2 h, times of approaching the alert area, times of falls, times of getting out of bed at night, times of staying in the toilet for over 15 min, times of emergency call for help, and sleep time and activities. The system conducted data analysis based on these indices to assist the caregiver in easily understanding the status of each subject, so as to improve the quality of care.

Satisfaction survey of the proposed intelligent health monitoring system was conducted to the caregivers and the subjects. The survey results showed that 95% of the caregivers would like to use the system, 97% of the caregivers thought that the system was easy to operate, 92% of the subjects would like to use the system, and 98% of the subjects thought that the smart clothing was comfortable and was willing to wear the smart clothing for a long time. In the future, the proposed intelligent health monitoring system will be applied and improved for healthy communities and home-based elderly care to achieve the purpose of elderly care in situ.

Discussion and conclusion

We have proposed a smart clothing–based intelligent health monitoring system. The system is mainly composed of smart clothing and sensing component, care institution control platform, and mobile device. The system can provide eight kinds of services for the elders, including surveillance of signs of life, tracking of physiological functions, monitoring of the activity field, anti-lost, fall detection, emergency call for help, device wearing detection, and device low battery warning. To our knowledge, such a system has not been reported in the literature.

The system has integrated our proposed Fast-EMD algorithm for ECG denoising and the HMM algorithm for fall detection. The Fast-EMD algorithm was improved based on the EMD algorithm for wearable devices to suppress motion artifacts. The HMM-based fall detection algorithm was originally proposed in our previous study.²⁶ However, the algorithm was implemented and evaluated in an off-line way in the study by Cao et al.²⁶ Compared with the study by Cao et al.,²⁶ the improvements in this work were as follows: (1) we implemented the HMM-based fall detection algorithm in the wearable device and (2) the algorithm was evaluated in an online manner through the proposed intelligent health monitoring system. The performance of the Fast-EMD and HMM algorithms have been evaluated by experiment I and experiment II, respectively. The evaluation results demonstrate the satisfying performance of the proposed Fast-EMD and HMM algorithms.

In recent work,²⁹ a remote health monitoring system for the elderly based on smart home gateway was introduced, which consisted of three parts: smart clothing, smart home gateway, and health care server. Compared with the work by Guan et al.,²⁹ we have achieved the following improvements in this work. The smart clothing–based intelligent health monitoring system in this work integrated our proposed Fast-EMD algorithm for ECG signal denoising and the HMM-based fall detection algorithm, supporting some of the eight kinds of services provided by the system. Moreover, the proposed system was applied and evaluated in a real elderly care institution, with satisfying performance.

In conclusion, we have designed and developed an intelligent health monitoring system based on smart clothing in this work. A Fast-EMD-based ECG denoising method and an HMM-based fall detection method have been proposed and integrated into the system. The system can provide eight kinds of services to the elderly. The proposed intelligent health monitoring system has been applied and evaluated in a real scenario, that is, an elderly long-term care institution. The results of the satisfaction survey show that both the caregivers and the elders are willing to use the system. The future direction of the proposed intelligent health monitoring system is elderly care in situ for healthy communities and home-based elderly care.

Footnotes

The authors would like to thank the anonymous reviewers for their insightful comments and suggestions.

Handling Editor: Kenneth Loh

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This work was supported by Ministry of Science and Technology (National Science Council) of Taiwan (Grant Nos. MOST 104-2218-E-182-005-MY3 and MOST 105-2627-E-182-001) and Chang Gung Memorial Hospital (Grant No. CMRPD3E0063). S.W. was supported by National Natural Science Foundation of China (Grant No. 71661167001). Z.Z. was supported by Beijing Natural Science Foundation (Grant No. 4184081),China Postdoctoral Science Foundation (Grant No. 2017M620566),Postdoctoral Research Fund of Chaoyang District,Beijing (Grant No. 2017ZZ-01-03),and Basic Research Fund of Beijing University of Technology.

References

World Health Organization. World report on ageing and health, 2015, http://www.who.int/ageing/events/world-report-2015-launch/en/ (accessed 30 March 2018).

Nguyen

Truong

Jeong

GM.

Daily wrist activity classification using a smart band. Physiol Meas 2017; 38(9): L10–L16.

Eun

Whangbo

Park

et al . Development of personalized urination recognition technology using smart bands. Int Neurourol J 2017; 21(Suppl 1): S76–S83.

Axisa

Schmitt

Gehin

et al . Flexible technologies and smart clothing for citizen medicine, home healthcare, and disease prevention. IEEE Trans Inf Technol Biomed 2005; 9(3): 325–336.

Chen

Song

et al . Smart clothing: connecting human with clouds and big data for sustainable health monitoring. Mobile Netw Appl 2016; 21(5): 825–845.

Wang

Lin

et al . Wireless sensor-based smart-clothing platform for ECG monitoring. Comput Math Methods Med 2015; 2015: 295704.

Zhang

Tian

Marindra

AMJ

et al . A review of passive RFID tag antenna-based sensors and systems for structural health monitoring applications. Sensors 2017; 17(2): 265.

Aslam

Khan

Azam

et al . Miniaturized decoupled slotted patch RFID tag antennas for wearable health care. Int J RF Microw C E 2017; 27(1): e21048.

Vazquez

Garibaldi

Nieto

et al . Model for personalization of mobile health systems for monitoring patients with chronic disease. IEEE Lat Am Trans 2016; 14(2): 965–970.

10.

Mahmud

Wang

Esfar

et al . A wireless health monitoring system using mobile phone accessories. IEEE Internet Things J 2017; 4(6): 2009–2018.

11.

Hussein

Hashim

Aziz

AFA

et al . A real time ECG data compression scheme for enhanced Bluetooth low energy ECG system power consumption. J Ambient Intell Human Comput. Epub ahead of print 17 August 2017. DOI: 10.1007/s12652-017-0560-y.

12.

Pigini

Bovi

Panzarino

et al . Pilot test of a new personal health system integrating environmental and wearable sensors for telemonitoring and care of elderly people at home (SMARTA project). Gerontology 2017; 63(3): 281–286.

13.

Majumder

Aghayi

Noferesti

et al . Smart homes for elderly healthcare—recent advances and research challenges. Sensors 2017; 17(11): 2496.

14.

Wang

Yang

Dong

A review of wearable technologies for elderly care that can accurately track indoor position, recognize physical activities and monitor vital signs in real time. Sensors 2017; 17(2): 341.

15.

Kekade

Hseieh

Islam

et al . The usefulness and actual use of wearable devices among the elderly population. Comput Methods Programs Biomed 2018; 153: 137–159.

16.

Baig

GholamHosseini

Moqeem

et al . A systematic review of wearable patient monitoring systems—current challenges and opportunities for clinical adoption. J Med Syst 2017; 41(7): 115.

17.

Yao

Skilskyj

Huang

. A compact, low-cost, real-time interrogation system for dynamic interrogation of microstrip patch antenna sensor. In: Proceedings of the SPIE smart structures and materials nondestructive evaluation and health monitoring, Denver, CO, 27 March 2018, vol. 10598, p.105980Y. Bellingham, WA: SPIE.

18.

Sundaravadivel

Mohanty

Kougianos

et al . Smart-walk: an intelligent physiological monitoring system for smart families. In: Proceedings of the IEEE international conference on consumer electronics (ICCE), Las Vegas, NV, 12–14 January 2018, pp.1–4. New York: IEEE.

19.

Zhang

Amft

Monitoring chewing and eating in free-living using smart eyeglasses. IEEE J Biomed Health Inform 2018; 22(1): 23–32.

20.

Daher

Diab

El Najjar

MEB

et al . Elder tracking and fall detection system using smart tiles. IEEE Sensors J 2016; 17(2): 469–479.

21.

Yao

Tjuatja

Huang

. A compact FMCW interrogator of microstrip antenna for foot pressure sensing. In: Proceedings of the progress in electromagnetic research symposium (PIERS), Shanghai, China, 8–11 August 2016, pp.2101–2105. New York: IEEE.

22.

Blanco-Velasco

Weng

Barner

KE.

ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Comput Biol Med 2008; 38(1): 1–13.

23.

Jain

Bajaj

Kumar

Riemann Liouvelle fractional integral based empirical mode decomposition for ECG denoising. IEEE J Biomed Health Inform 2018; 22: 1133–1139.

24.

Lee

McManus

Merchant

et al . Automatic motion and noise artifact detection in Holter ECG data using empirical mode decomposition and statistical approaches. IEEE Trans Biomed Eng 2012; 59(6): 1499–1506.

25.

Huang

Shen

Long

et al . The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. P Roy Soc A: Math Phy 1998; 454(1971): 903–995.

26.

Cao

Zhou

et al . A fall detection method based on acceleration data and hidden Markov mode. In: Proceedings of the IEEE international conference on signal and image processing (ICSIP), Beijing, China, 13–15 August 2016, pp.684–689. New York: IEEE.

27.

Rabiner

LR.

A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 1989; 77(2): 257–286.

28.

Duda

Hart

Stork

DG.

Pattern classification. Hoboken, NJ: John Wiley & Sons, 2012.

29.

Guan

Shao

A remote health monitoring system for the elderly based on smart home gateway. J Healthc Eng 2017; 2017: 5843504.

Intelligent health monitoring system based on smart clothing

Abstract

Keywords

Introduction

Materials and methods

System design

ECG signal denoising and heart rate calculation

Fall detection

Experiments and results

Experiment I: Fast-EMD evaluation

Experiment II: fall detection

Application in real scenario

Discussion and conclusion

Footnotes

Declaration of conflicting interests

Funding

References