Table 3.
Technical insights and overview of SF design for patient monitoring.
| Ref. No. |
Target Application |
Technical Details | Main Findings |
|---|---|---|---|
| [47] | Inertial and plan- tar pressure measurement |
Insole, wrist band, accelerometer, gyroscope, pressure sensor, BLE, smartphone, sampling rate 50 Hz. |
The best body part for HAR: Feet or wrists. |
| [48] | Six ambulation activities detection |
Smart insoles: accelerometer, gyroscope, magnetometer, ECU, BLE, ML algorithms, smartphone, 200 Hz, 120 min, 25–55 years. | Inertial sensors are reliable for dynamic and pressure sensors for stationary activities. |
| [53] | Foot pressure distribution |
Capacitive sensor, ML. | ML provides the required pressure measurement. |
| [50] | Plantar pressure and activity recognition |
Seven pressure sensors, FFT, ML, 100 Hz, 12-bit, 26 ± 9 years. |
Generalization is needed for larger populations. |
| [54] | Foot pressure and motion activities |
280 capacitive pressure sensors, 56 temperature sensors, FT, wired. | Smart insole alternative for activity recognition. |
| [55] | Plantar pressure – daily activity |
MWCNTs/PDMS piezoresistive nanocomposites, LAB View. | Useful for disease detection and diagnosis. |
| [56] | Daily activities recognition | Accelerometer, DL, wireless. | SF is user-friendly for all ages. |
| [58] | Locomotor activities | Accelerometer, gyroscope, magnetometer, FFT, CNN. | User-independent system for HAR possible. |
| [62] | Diabetic feet monitoring |
Temperature, humidity sensors, eight pressure sensors, BLE, Arduino 328, 25–55 years. | Improves self-management and health outcomes. |
| [64] | DFU prevention | Flexible insoles, 99 capacitance-based sensors, 50 Hz, 2 sensors/cm2, 919 patient’s databases. | Pre-clinical studies met user needs. |
| [67] | DFU monitoring: plantar pressure | Eight pressure sensors, a smartwatch, 8 Hz, and an age group greater than 18 years. | Continuous monitoring reduces DFU recurrence. |
| [68] | DFU: Plantar pressure measurement | Eight capacitive sensors, flexible PCB, BLE, microcontroller, 100 Hz, 28 bits. | Enhances efficiency in studying diabetic foot conditions. |
| [70] | DFU monitoring: Plantar pressure | Pressure sensor, PC, 50 Hz, 76 participants. | Optimization is needed for real-time use. |
| [71] | Diabetic foot monitoring |
Four temperature sensors, 35 participants. | Continuous monitoring provides preventative foot ulcer information. |
| [73] | DFU: pressure measurement | Nineteen female participants, 57–75 years, 4D scanner. | Custom insoles and heel pads help redistribute pressure. |
| [74] | DFU monitoring: Temperature, humidity |
Textile insole, silicon tubes, leather, five sensors, and 21–30 years of age females. | Textile insoles enhance thermal comfort. |
| [81] | Balance and gait analysis in older women |
Thirty women, 65–83 years, lab tests, ethyl vinyl acetate insoles. |
Significant reduction in step width observed. |
| [82] | Gait analysis and PD study | Pressure sensors, accelerometer, 29 participants, 100 Hz, 20–59 years. | Dataset valuable for detailed gait analysis. |
| [83] | Fall detection in elderly | Arduino Nano, sensors array, buzzer, vibration motor. | Smart shoes with devices detect and prevent falls. |
| [84] | Mobility and gait assessment |
Force sensing resistors, IMU, ultrasound sensor, Arduino, BLE. | Detects abnormalities in walking patterns. |
| [86] | Flat feet detection | Three force sensors, accelerometer, BLE, Arduino Nano. | Cost-effective alternative to motion capture systems. |
| [87] | Real-time gait monitoring |
Soft insole, capacitance-based pressure sensor, conductive textile, microcontroller, 100 Hz, 15 participants. | Textile-based insole alternative to smart shoes. |
| [90] | Portable gait analysis |
Piezoresistive sensor, IMU, logic unit, 500 Hz, 6 min recording, ML, 14 participants. | Learning-based methods improve gait parameters. |
| [91] | Gait parameters measurement |
Piezoresistive sensors, microcontroller, WIFI, IMU, 500 Hz, 9 participants, MAT- LAB. | Useful for out-of-lab gait analysis. |
| [92] | Foot progression angle estimation | Inertial, magnetometer units, accelerometer, gyroscope, 100 Hz, 14 participants, 22–29 years. | Useful for knee osteoarthritis monitoring in daily life. |
| [93] | Detecting changes in gait by alcohol intoxication |
Twenty participants, wireless mode, ML algorithm. | SF can be used for detecting alcohol-impaired gait. |
| [94] | Locomotion monitoring: real-time kinetic measurement |
Pressure sensors, IMUs, WIFI, Smartphone, PC, sampling rate: 100 Hz, 9 participants, MATLAB 2019b software. | Acceptable matches were achieved for the measured CoPx and the calculated knee joint torques out of 13 movements. |
| [95] | Plantar pressure measurement |
Capacitive sensor: silver and cotton, microchip, USB, laptop, BLE. | Gait phases and different patterns can be detected, and the system is bacterial-resistant. |
| [96] | Gait analysis tool: PODOS-mart® | IMUs: Sensors, 11 participants, age group: 20–49 years, BLE, sampling rate: 208 Hz. | Ease of use without technical education. |
| [97] | Evaluating haptic terrain for older adults and PD patients (TreadPort) | Five bladders, PC, VR terrain, WIFI, microcontroller, CAVE display, camera: 60 frames/sec. | Applicable for gait training for walking impediments caused by PD. |
| [98] | Locomotion monitoring: centre of pressure detection |
Five textile capacitive sensors, WIFI, sampling rate: 100 Hz, MATLAB R2021a software. | Smart wearable sensors can improve quality of life. |
| [99] | Designing and fabricating biomimetic porous graphene flexible sensor: gait analysis | Graphene nanoplates, SBR foam, silver electrodes, microcontroller, BLE. | The system can monitor older and can help with gait training. |
| [100] | Plantar pressure measurement: gait analysis | Twelve capacitive sensors: copper and poly-dimethyl siloxane, PIC microcontroller, BLE, PC. | The design offers correct performance behaviour under footfall. |