Wearable Sensors for Remote Health Monitoring
Abstract
:1. Introduction
2. Wearable Health Monitoring Systems
2.1. Cardiovascular Monitoring System
2.2. Activity Monitoring System
2.3. Body Temperature Monitoring System
2.4. Galvanic Skin Response (GSR) Monitoring System
2.5. Blood Oxygen Saturation (SpO2) Monitoring Systems
2.6. Multi-Sensor Monitoring System
3. Textile-Based Wearable Sensors
3.1. Textile Electrodes
3.2. Textile Based Temperature Sensors
3.3.Textile Sensors for Activity Measurement
4. Communication Technologies for Wearable Systems
5. Conclusions and Research Challenges
Acknowledgments
Conflicts of Interest
References
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Product Name | Monitored Parameters | Wireless Platform | Battery | |
---|---|---|---|---|
Type | Life | |||
Hexoskin® Biometric® Shirt | Heart rate (HR), HR variability, respiratory rate, number of steps, distance traveled, pace, maximal oxygen consumption, and calories burned. | Bluetooth | 6–7 days (standalone) 14+ h (multi-training) | |
Jawbone UP3™ Fitness Tracker | Sleep stages (REM, light and deep), HR, food and liquid intake, number of steps, distance traveled, running. | Bluetooth LE | Li-ion poly | 7 days |
Striiv® Fusion Bio Fitness Tracker | HR, number of steps, distance traveled, calories burned, and sleep quality. | Bluetooth LE | Li-ion | 5 days |
Microsoft® Band 2 | HR, calories burned, sleep quality, food, and liquid intake, number of steps, elevation, climbing, running, biking. | Bluetooth | Li-poly | 2 days |
Fitbit Charge HR™ Fitness Tracker | HR, calories burned, sleep quality, food, and liquid intake, number of steps, elevation, climbing, running. | Bluetooth LE | Li-poly | 5–7 days |
Garmin vivosmart® HR Fitness Tracker | HR, calories burned, sleep quality, number of steps, climbing, running, swimming. | Bluetooth LE, ANT+ | Li-ion | 5 days |
Ref. | Proposition | Moni-Tored Signs | Electrode Type | Active Material | Electrode Size | Attachment Method | Wireless Connectivity | Accuracy | Signal Acquisistion Module | |||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Size | Freq. | A/D | Bat. Life, Power | |||||||||
[28] | Sensorized T-shirt and textile belt | ECG, HR | Dry textile electrodes | Silver based conductive yarns | Snap buttons | Bluetooth LE | - | 512 Hz | 24 bit | - | ||
[29] | Wearable mobile electro-cardiogram monitoring system | ECG, HR, location | Dry foam electrode | Ni/Cu coated compressed urethane polymer foam | 14 mm × 8 mm × 8 mm | Bluetooth v2.0, and GSM | 99.51% correlation with prerecorded ECG data, QRS detection accuracy ~98.14% | 4 cm × 2.5 cm × 0.6 cm | 512 Hz | 12 bit | 33 h, 1100 mAh Li-ion battery | |
[10] | Wireless, portable capacitive ECG sensor | ECG, HR | Capacitive electrode with cotton insulator | 33 mm × 33 mm × 2 mm | Woven under a stretchable belt | ANT | 45 mm × 60 mm × 9 mm | 500 Hz | 10 bit | 15 h with 256 mAh 3 V Li battery | ||
[30] | Use of flexible capacitive electrodes for reducing MA | ECG, HR | Flexible capacitive electrodes | Ni/Cu coated foam (polyolefincovered by polyurethane) | 300 mm × 20 mm × (1.1 ± 0.2 mm) | Integrated into a chest belt | Bluetooth | Upto 91.32% QRS detection at 7 km/h walking speed | - | 256 Hz | - | - |
[31] | Common Electrode-FreeECG monitoring System | ECG, HR | Active capacitive electrodes | Copper layer | 5 cm × 3 cm | Adhesive tape | - | - | 2 kHz | 24 bit | - | |
[32] | HR monitoring from pressure variance in ear canal | HR | Piezo-electric film sensor | 3.5 mm × 3.5 mm | Earpiece like device | 2.4 GHz RF | Sensitivity 97.25%, PPV 97.18%. | 15 mm × 17 mm | 100 Hz | 12 bit | Coin-cell battery | |
[33] | Heart Rate Monitoring with pressure sensor | HR | Piezo-resistive pressure sensing | C black/silicone rubber nanocomposite encapsulated in conductive FCCL films | 15 mm × 30 mm | Embedded in elastic belt | - | Accuracy > 97% | - | - | - | - |
Spatial Domain | Temporal Domain | Frequency Domain | Statistical Domain |
---|---|---|---|
Step length | Double support time | Spectral power | Correlation |
Stride length | Stance time | Peak Frequency | Mean |
Step width | Swing time | Maximum spectral amplitude | Standard deviation |
RMS acceleration | Step time | Covariance | |
Walking speed | Stride time | Skewness | |
Cadence (steps/min) | Kurtosis | ||
Energy |
Ref. | Proposition | Feature Extraction | Classification Method | Sensors | Sensor Placement | Com. Tech. | Detection | Accuracy | Power Req. |
---|---|---|---|---|---|---|---|---|---|
[44] | Activity and gait recognition system on a smartphone | Fixed set of features | Support Vector Machine (SVM), Bayes network, and Random Tree | Accelerometer is embedded in smartphone | Different walking speed | >99%. | |||
[45] | In-home, fine-grained activity recognition multimodal wearable sensors | Fixed feature set | Conditional random field (CRF) | Smartphones’ (Samsung Galaxy S4) onboard sensors (accelerometer, gyroscope, barometer, temperature and, humidity sensor), along with Gimbal Bluetooth beacons | Waist, lower back, thigh, and wrist | USB | Walk and run indoors, use refrigerator, clean utensil, cook, sit and eat, use bathroom sink, move from indoor to outdoor, move from outdoor to indoor, walk upstairs, and walk downstairs, stand, lie on the bed, sit on the bed, lie on the floor, sit on the floor, lie on the sofa, sit on the sofa, and sit on the toilet | 19 in-home activities with >80% accuracy | |
[46] | Wearable device based on a 9-DOF IMU | Fixed set of features | Accelerometer, gyroscope, and magnetometer | Limb or trunk | Bluetooth | Balance hazards, balance monitoring for fall prediction | High correlation | Streaming ~6 h Logging > 16 h | |
[47] | Algorithm development | Time-Frequncy domain analysis | Hidden Markov Model | 3-axis accelerometer, 3-axis gyroscope | Chest | USB | Walking, running, ascending upstairs, descending downstairs and standing | ~95% | |
[48] | A real-time, adaptive algorithm for gait-event detection | Two inertial and magnetic sensors ( 1 IMU = 1 accelerometer, 1 gyroscope) | External part of both shanks | Gait events: Initial Contact (IC), End Contact (EC) and Mid-Swing for both right and left leg while walking at three different speed | F1-scores 1(IC, EC), 0.998 (IC) and 0.944 (EC) for stroke subjects | ||||
[49] | Recognition method for similar gait action | Inter-class relation Ship | Support vector machine, K-nearest neighbor | 3 IMUs (each IMU: 1 tri-axis accelerometer,1 tri-axis gyro) | Fixed at the back, left, and right waist | Walking on flat ground, up/down stairs, and up/down slope | ~93% average | ||
[50] | Stochastic approximation framework | Fixed set of features | K–means and Gaussian Mixture Models | Accelerometer | Belt-like strap around the waist | 3 intensity level of walking: 93.8%; 3 intensity level of running 95.6% | |||
[51] | Power-aware feature selection for minimum processing energy | Minimum cost feature selection by using a redundancy graph | K-nearest neighbor | 6 IMUs (each IMU has one three-axis accelerometer and a two-axis gyroscope) | Waist, right wrist, left wrist, right arm, left thigh, right ankle | BSN | Switching between stand and sit, sit and lie, bend to grasp, rising from bending, kneeling right, rising from kneeling, look back and return, turn clockwise, step forward and backward, jumping | 30% energy savings with 96.7% accuracy | |
[52] | Parameter optimization strategy for phase-dependent locomotion mode recognition | Fixed set of features | 2 IMUs, 2 pressure insoles (each having 4 pressure sensors) | IMUs on the shank and the shoe, pressure sensors insole | Walking, up/down stairs, and up/down slope, passive mode | 88%–98% | |||
[53] | Electronic insole for wireless monitoring of motor activities and shoe comfort | Fixed set of features | Humidity and temperature sensors, accelerometer and 4 pressure sensors | Insole | ZigBee | Foot accelerations, orientation in space, temperature and moisture data | 10 h of data logging | ||
[54] | Shoe-based activity monitoringsystem (smartshoe) | Fixed set of features | Support vector machine, multilayer perception (MLP) | Five pressure sensors (PS) and one 3-D accelerometer | PS on insole and accelerometer on heel of shoe | Sit, stand, walk, ascend stairs, descend stairs and cycling | 99.8% ± 0.1% with MLP | ||
[55] | A wearable device for monitoring daily use of the wrist and fingers | Fixed set of features | K-means | 2 tri-axial magnetometers | Watch-like enclosure worn on the wrist and a small neodymium ring worn on the index finger | Finger and wrist movement | 92%–98% with a 19%–28% STD | 20.5 mA at 3.3 V | |
[56] | Combined kinematic models to estimate human joint angles | Unscented Kalman filter | 3 IMUs | Upper arm, forearm, and wrist | Shoulder internal/external rotation; flexion/extension of shoulder, elbow, and wrist, supination/pronation of forearm, wrist twist | Average RMS angle error ~3° | |||
[58] | Wearable device with automatic gait and balance analyzing algorithms for Alzheimer patients (AP) | Fixed set of features | 3 IMUs (each IMU has a 3-d accelerometer, a uni-axial gyroscope, and a biaxial gyroscope | On feet for gait analysis on waist for balance analysis | Gait parameters and balance | 30 mA at 3.7 V | |||
[59] | IMU based fall Detection system | Madgwick orientation filter | Accelerometer, gyroscope, and magnetometer | Waist | Bluetooth | Backward fall, forward fall, lateral left fall, lateral right fall, syncope | Accuracy: 90.37%–100% Sensitivity: 80.74%–100% | 15 mA–34 mA using 3.7 V |
Ref. | Proposed Device | Principle | Measured Parameters | Used Device for Measurement | Location | Wireless Connectivity | Performance Evaluation | Accuracy |
---|---|---|---|---|---|---|---|---|
[66] | Kalman filter based body temp. estimation model | Temperature variation with HR | HR, skin temperature | Ag/AgCl gel electrodes | Chest | - | Compared with data from ingestible temperature capsule | RMSE: 0.40 °C |
[68] | Wireless, dual channel body temp measurement system | Mean of measurements from two ear canals | Core body temp | Digital temp sensor DS18B20 | Ear canal | Bluetooth | ±0.1 °C | |
[69] | Wearable wireless temperature monitoring | Two-point calibration | Circadian rhythms, Skin temp | MF51E NTC thermistor | Skin | RF (Tyndall node) over Body sensor network (BSN) | Compared with data from a thermometer | 0.02 °C |
[71] | Embedded NTC temperature sensor and conductive textile wires in a belt made with soft bamboo | ECG, skin temperate | NTC Mon-A-Therm 90045 and Shieldex® Silver Plated Nylon yarn | Skin | - | Compared with data from the NICU sensor connected to the Solar® 8000M patient monitor | ±0.1 °C | |
[72] | Wireless body temperature monitoring | Skin temperature | LM35 | Hand | ZigBbee and WLAN | ±0.25 °C | ||
[75] | RFID sensor chip in 0.35-μm CMOS standard process | Temperature dependence of the frequency of ring oscillator | Tag and reader communicate at 868 MHz | Measurement was performed in a climate chamber | ~±0.1°C Resolution: 0.035°C | |||
[76] | Epidermal-like RFID tag made on a Poli-caprolactone membrane | Re-tunable epidermal tag | Skin temperature | EM4325 | Abdomen | Tag and reader communicate within a band of 780–950 MHz | Compared with data from PT104 thermocouple | ±0.25 °C |
[77] | Deep body temperature measurement system embedded in a neck pillow | Embedding 1 Dual-heat-flux, 2 double-sensor in neck pillow | Core body temperature | Around neck | - | Compared with data from infrared thermometer (thermoscan IRT 4520) | - | |
[79] | Heater-less deep body temperature probe | Dual-heat-flux method | Core body temperature | Forehead | - | Compared with data from zero-heat-flow thermometers | Correlation: 97% |
Ref. | Proposition | Electrode Type/Device | Measurement Location | Wireless Connectivity | Size | Sampling Rate | A/D | Battery Life/Power Req. | Evaluation | Accuracy |
---|---|---|---|---|---|---|---|---|---|---|
[85] | A small wristband for unobtrusive and continuous EDA measurements during everyday activities | Ag/AgCl electrodes | Dorsal forearms | 2.4 GHz transceiver module (nRF2401) | 70 mm × 70 mm × 20 mm | 32 Hz | 12 bit | 1199 mAh, 3.7 V LiPo | Measurement compared with commerecial system. | overall correlation: 93%–99% |
[86] | An ambulatory device for measuring HR, GSR, and skin temperature | Arduino based e-textile lilypad platform (SHT15 for T measurement) | Not implemented | Supply voltage: 2 V to 5 V | ||||||
[87] | Highly wearable and reliable galvanic skin response (GSR) sensor | flexible dry polymer foam Ni/Cu | Back | Bluetooth | 42.5 mm × 38.5 mm | 10 bit | compared thesignal with a finger reference GSR | average Correlation: 76.8% | ||
[88] | Wearable multi-sensor device for real-time biofeedback and data acquisition | Ag electrodes | Bluetooth LE | 4 cm × 4 cm | 4 Hz | 38 h of operation | resolution 900 pS between 0.01 µS and 100 µS | |||
[89] | A pervasive and unobtrusive system for sensing human emotions | Commercial Shimmer GSR sensor | Finger | Bluetooth | 65 mm × 32 mm × 12 mm | 10 Hz | 450 mAh Li-ion battery | Classification of 4 emotions with ~80% of accuracy (amusement, fear, sadness, and relaxation) | ||
[90] | Distinguishing stress from cognitive load in an office environment by EDA | Dry Ag/AgCl electrodes | left index and middle fingers | Bluetooth | 41 mm × 67 mm | 16 Hz | Power consumption: 182 mW | Investigated 6 classifiers to discriminate cognitive load from stress | Accuracy 82.8% (max), achieved by LDA | |
[91] | Use of wearable sensors and wireless technology to measure the autonomic function and stress level in the ambulatory setting | Ag/AgCl electrodes in Shimmer Platform | Palm of non-dominant hand | Bluetooth | 30 Hz | GSR preconditioning circuit consumes 60 µA | ||||
[93] | A wearable device for predicting blood pressure (BP) and cardiovascular dynamics | Ag/AgCl electrodes | Fingers or opposite sides of palm | Bluetooth | 1280 Hz, averaged over 32 samples: results 40 Hz | 10 bit | 10 h with 9 V battery, 220 mA with Bluetooth | correlation with pulse pressure with GSR | R2 value for PP: 0.923, SBP: 0.801 |
Ref. | Proposition | Principle | Measured Parameters | Sampling Rate | Size | Power/Current Req. | Wireless Connectivity | Performance Evaluation | |
---|---|---|---|---|---|---|---|---|---|
[97] | Ring shaped backside silicon p-n photodiode | Transmittance oximetry | Temperature, Pulse, SpO2 | 8 kHz | Radius = 3.68 mm width = 0.78 mm | <10 mA | Quantum eff. = 62% Reverse current density = 55 nA/cm2 Forward saturation current = 0.14 nA/cm2 | ||
[98] | Sensors embedded in soft fabrics | Reflectance oximetry | HR, SpO2 | Measurement compared graphically with commercial oximeter measurements | |||||
[99] | Wireless oximeter | Reflectance oximetry | HR, RR, SpO2, PPT | 240 Hz | 41mm × 36 mm | <150 mA | ZigBee | SNR of IR = 8 SNR of red =3 | |
[100] | Micro-machined Pt electrodes | Transmittance oximetry | ECG, HR, SpO2 and SBP | 200 Hz | <35 mA | ZigBee | |||
[102] | Ring probe, novel distribution of optical sensors around the phalanx | Transmittance oximetry | HR, SpO2 | Diameter of the finger | Measurement compared graphically with commercial oximeter measurements | ||||
[103] | Wrist band Sensor | Reflectance oximetry | HR, SpO2 | CC2500 RF TRX | Ratio of change rates of reflected light intensity in two wavelengths (660 nm and 900 nm) | ||||
[104] | Ring-type pulse oximeters | Reflectance oximetry | HR, RR, SpO2, PPT | Bluetooth | Correlation between SpO2 values measured by the proposed and commercial oximeter | 98.26% | |||
[107] | Analog single-chip pulse oximeter | SpO2 | 2.2 mm × 2.2 mm | 4.8 mW | Measurement compared with commercial oximeter measurements | Mean diff. ~−1.2% SD = 1.5% | |||
[108] | Forehead mounted sensor | Reflectance oximetry | HR, SpO2 | WiFi | Measurement compared with commercial oximeter measurements | ||||
[109] | Electronic Patch with an optical biomedical sensor | Reflectance oximetry | PPG, HR, RR | 125 Hz | 88 mm × 60 mm (× 5 mm) | I < 33 mA P < 99 mW | PPG is measured using Datex pulse oximeter. SpO2 is calculated and plotted against optical ratio for calibration, MSE ~ 2.6% |
Ref. | Proposition | Electrode Type | Size | Base Material | Conductive Material | Technology | Performance | Contact Resistance |
---|---|---|---|---|---|---|---|---|
[131] | Direct attach and Interposer electrode | Active electrode | 20 × 13 mm2 (direct-attach) 11.6 × 11.6 mm2 (Interposer) | Nonwoven Evolon fabrics | Conductive ink (CMI 112-15) | Screen printing, stenciling, curing, and encapsulation | *PSDs for sitting and jogging are close to Ag/AgCl electrodes*Durable upto 5 washing cycles | |
[136] | Active electrodes on woven textiles | Active electrode | 28 mm × 23 mm (skin contact area) | Woven textile composed of cotton, polyester and Lycra fibers | Silver polymer paste (Fabinks TC-C-4001) | Screen and stencil printing | The printed active and Ag/AgCl electrodes had very similar rms levels after filtering | |
[138] | 2 textile nanofiber web electrodes | Dry electrode | 9 mm diameter | PVDF Nanofiber Web | Poly (3,4-ethylene-dioxythiophene) (PEDOT) | Electrospinning-vapor phase polymerization | Tested ECG is 95% similar to Ag/AgCl electrodes | ~1000 Ω |
PVDF Nano fiber Web | Silver | Silver mirror reaction | Tested ECG is ~92% similar to Ag/AgCl electrodes | ~100 Ω | ||||
[139] | Nano copper loaded poly-propylene based textile electrode | Dry electrode | 4 cm × 6 cm | Polypropylene nonwoven fabric | Copper nanoparticles on fabric | Multiple dip chemical processes | Max conductivity: 142.8 kΩ·m | |
[143] | 8 types of electro-thread | Dry fabric electrode | 2 × 2 cm2, 2 × 5 cm2 | Polyester 75 denier | Silver thread | Inclusion of one strand or two strands of 50 μm silver thread | 32 kΩ at 120 Hz (for 2 Ag strand based 1300TM polyester fabric) | |
[141] | Several textile-based electrodes | Dry fabric electrode | 1.5 cm × 3 cm | PU laminated or dry- coated nylon | Copper coating | Sputtering | 5.7 Ω (PU laminated nylon), 10.26 Ω (PU dry-coated nylon). | |
Ripstop, Mesh fabric | Cu/Ni coating | Electroless Plating | 0.23 Ω/sq (Ripstop), 0.29 Ω/sq (Mesh) | |||||
5 cm × 5 cm | Cotton, Steel/cotton | Stainless Steel Filament Yarn | Embroidering or Knitting | R peak detection accuracy: 58.8% and 64.2% | 32.55 Ω/m (linear resistance) | |||
[142] | Knitted fabric electrodes | Dry electrodes | 20 mm × 20 mm | Wool and polyester | Silver coated nylon, stainless steel yarn, and silver coated copper | Knitting | FFT response of the multifilament electrodes retains ECG spectralcomponents | |
[144] | Embroidered textile electrode | Wet, moisturized by water vapor using the polyester wetting pad. | 2 cm × 7 cm | Polyethylene terephthalate yarn of 50 μm diameter | Silver and ultra-thin titanium | Coating by plasma sputtering | Similar signal quality and signal strength after 1 h as after 72 h of use |
Ref. | Proposition | Type | Fabrication Method | Temperature Range | Sensing Material | Sensitivity | Size | Substrate/Embedding Platform | Performance | Nominal Resistance |
---|---|---|---|---|---|---|---|---|---|---|
[160] | Polymer sensor | Thermistor | Screen printing | Carbon polymer paste | Polyamide foil Kapton | High flexibility, linear characteristic, high thermal resistance change | ||||
[166] | Optical fiber Bragg grating based sensor | Optical | Grinding, polishing | 33 °C to 42 °C | Fiber Bragg grating | 0.15 nm/°C | Encapsulated with polymer (copolymerization of unsaturated Methyl Ethyl Ketone Peroxide (MEKP) and cobalt naphthenate) filled strip. | Accuracy ~± 0.18 °C | ||
[165] | Printed sensors on flexible substrate | RTD | Screen printing | 20 °C to 80 °C | PTC and NTC resistive pastes | 0.025 V/°C at 37 °C | 320 mm × 380 mm | Poly Ethylene Naphtalate (PEN) | ||
[161] | Inkjet printed flexible sensor | Thermistor | Inkjet printing | 20 °C to 60 °C | Silver | 4.5 Ω/°C at 38.5 °C | 2.85 cm × 2.26 cm | Polyimide substrate (Kapton HN) | Good linearity (coefficient of linearity ~ 0.9998) Hysteresis less than 5% | 2.032 kΩ at 38.5 °C |
[158] | Arrays of single sensors on a flexible substrate | RTD | Electron beam evaporation followed by photolitho-graphy | 25°C to 90°C | Meander shaped structures of platinum | 1.52 Ω/°C | 67.5 mm × 67.5mm | Kapton E foils, Integrated into textile using weaving | The sensors damage at strong bending of around 11% due to cracking of the sensing lines | |
[164] | Sensors on paper substrate | RTD | Inkjet printing | −20 °C to 60 °C | Silver nano-particles | 16 mm × 16 mm | Nano-porous oxide film coated paper | Good linearity with a TCR of 0.0011/°C, with perylene coating linearity, is 0.9999, resistivity 30 µΩ·cm | 740 Ω with perylene coating | |
[159] | Embroidered sensors | RTD | Embroidery | 20 °C to 100 °C | Conductive yarn made of austenitic Cr-Ni stainless steel wires | 2.68 Ω/°C | 90 mm × 90 mm | Embroidered on a textile substrate | Good resistance against washing cycles | |
[162] | Printed wearable sensor | RTD | Shadow mask printing | 22 °C to 50 °C | Mixer of carbon nanotube and PEDOT:PSS | 0.6 %/°C | SiO2-coated Kapton | Good stability, highly sensitive | ||
[163] | Ultrasensitive wearable sensor | RTD | PECVD and polymer-assisted transfer method | 35°C to 45°C | Grapheme nanowalls | 20 mm × 10 mm | Polydimethylsiloxane (PDMS) | TCR = 0.214/°C, response time 1.6 s and recovery time 8.52 s | 706.2 Ω at 25 °C | |
[153] | Flexible wireless sensors | RTD with integrated passive RFID antenna | 35 °C to 42 °C | Ni microparticle- filled binary polymer (polyethylene (PE) and polyethylene oxide (PEO)) composites | 0.1 to 0.3 V/°C | Accuracy ~± 2.7 °C | ||||
[157] | Temperature sensing fabric | RTD | Metal wire inlaid in the middle of a rib knitted structure | 20 °C to 50 °C | Platinum wire, Diameter < 25 mm | 8 cm × 8 cm | Polyester fabric | Coefficient linearity in the range of 0.99–0.999 | 3 Ω to 130 Ω |
Ref. | Proposition | Sensing Mechanism | Structure/Base | Sensing Material | Gauge Factor | Stable Strain Range | Demonstrated/Potential Applications |
---|---|---|---|---|---|---|---|
[171] | Textile-structured flexible strain sensor | Contact resistance of fiber/yarn/fabric | Single warp fabric | Carbon fiber | 10–200 depending on fiber length | Max 200% | Wearable strain sensor |
[172] | Textile-based strain sensor | Contact resistance of conductive fiber loops | Fabric with elastomeric yarns | Silver coated polymeric yarn made loops | 0.75 | 40% | Wearable strain sensor |
[168] | Stretchable and Sensitive Strain Sensor | Piezoresistive | PDMS | Ag nano-walls thin film | 2 to 14 | 70% | Finger movements |
[170] | Textile-based strain sensor for monitoring the elbow and knee movements | Piezoresistive | Elastic yarns made from Lycra fiber wrapped with two polyester yarns. | Carbon particles coated polyamide fiber twisted with polyester yarn | ~0.3 | 30% | Flexion angle of elbow and knee movements |
[169] | Stretchable strain sensor based on a metal nanoparticle thin film for human motion detection | Piezoresistive | PDMS | Silver nanoparticle | 2.5 | 20% | Finger movements |
[175] | Knee’s kinematic monitoring using single optical FBG sensor | Fiber Bragg grating | Optical Fiber | Polymer encapsulated FBG sensor | ~0.8 | 0.04% | Knee, finger movements, HR, RR |
[176] | Force sensors based on light pipes in the form of multimode optical fibers made of copolymers. | Loss of light due to deflection of the fiber with force | Multimodal optical fiber | Copolymers containing silicon and polyurethane | Force sensing | ||
[167] | Textile-based MEMS accelerometer | Piezoresistive | Cotton fiber | Silver nanoparticles | 7.796 ± 2.835 | Motion sensing | |
[174] | All-polymeric knitted textile strain sensor | Piezoresistive | Commercial Spandex yarn | PU/PEDOT:PSS fibers | 0.2 to 1 | 160% | Knee bending movements |
Wireless Technology | Frequency Band | Range | Data Rate | Power Consumption | Maximum Number of Nodes Supported | Supported Network Topologies | Security | Modulation | Reference |
---|---|---|---|---|---|---|---|---|---|
RFID | 13.56 MHz 860–960 MHz | 0-3 m | 640 kbps | 200 mW | 1 at a time | P2P (passive) | N/A | ASK, PSK, FSK | [73,74,196] |
Bluetooth | 2.4–2.5 GHz | 1–100 m | 1–3 Mbps | 2.5–100 mW | 1 master + 7 slave | P2P, star | 56–128 bit key | GFSK | [29,30,45,59,66,87,90,91,92,93,104] |
BLE | 2.4–2.5 GHz | 1–100 m | 1 Mbps | 10 mW | 1 master + 7 slave | P2P, star | 128-bit AES | GFSK | [28,88] |
ZigBee | 2.4–2.5 GHz | 10–100 m | 250 kbps | 35 mW | 65,533 | P2P, star, tree and mesh | 128-bit AES | OQPSK, BPSK | [70,99,100] |
WiFi | 2.4–2.5 GHz | 150–200 m | 54 Mbps | 1 W | 255 | P2P, star | WEP, WPA, WPA2 | BPSK, QPSK, QAM | [108] |
UWB | 3.1–10.6 GHz | 3–10 m | 53–480 Mbps | 250 mW | 1 master + 7 slave | P2P, star | BPPM, FSK | ||
ANT | 2.4–2.5 GHz | 30 m | 20–60 kbps | 0.01–1 mW | 65,533 in one channel | P2P, star, tree and mesh | 64-bit key | GFSK | [10,19,188] |
MICS | 402–405 MHz | 2 m | 200–800 kbps | 25 µW | P2P, star | FSK | |||
IrDA | 38 kHz | 10 cm | 1 Gbps | 1 at a time | P2P | ||||
NFC | 13.56 MHz | 5 cm | 424 kbps | 15 mW | 1 at a time | P2P | AES | ASK |
© 2017 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
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Majumder, S.; Mondal, T.; Deen, M.J. Wearable Sensors for Remote Health Monitoring. Sensors 2017, 17, 130. https://doi.org/10.3390/s17010130
Majumder S, Mondal T, Deen MJ. Wearable Sensors for Remote Health Monitoring. Sensors. 2017; 17(1):130. https://doi.org/10.3390/s17010130
Chicago/Turabian StyleMajumder, Sumit, Tapas Mondal, and M. Jamal Deen. 2017. "Wearable Sensors for Remote Health Monitoring" Sensors 17, no. 1: 130. https://doi.org/10.3390/s17010130