A Survey of the Diagnosis of Peripheral Neuropathy Using Intelligent and Wearable Systems
Abstract
:1. Introduction
- What research has been carried out towards developing systems based on wearable devices for the diagnosis of peripheral neuropathy?
- Which types of wearable devices are the most suitable or commonly used for the diagnosis of peripheral neuropathy?
- How can wearable technology assist physicians and contribute to improving the health of patients having peripheral neuropathy or at risk of developing peripheral neuropathy?
- What are the challenges that wearable devices are facing in the diagnosis of peripheral neuropathy?
2. Methodology
3. Intelligent Wearable Systems Using Single-Sensor Type for Diagnosis of Peripheral Neuropathy
3.1. Wearable Inertial Sensor-Based Intelligent Systems for the Diagnosis of PN
3.2. Pressure Sensor-Based Intelligent Wearable System for Diagnosis of PN
3.3. ECG-Based Intelligent Wearable Systems for Diagnosis of PN
4. Intelligent Wearable Multisensory Systems for Diagnosis of PN
5. Discussion
6. Open Challenges and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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---|---|---|---|---|---|---|
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Wang et al. [53] | 49 | PN: 9 Stroke: 13 PD: 14 H: 13 | IMUs | Two IMUs were attached to the ankle of each shank | 12-m walking trail | The gait parameters were extracted using method [74] based on wavelet analysis. |
Cao et al. [59] | 56 | DPN: 19 Diabetic: 17 H: 20 | Insole wireless plantar pressure monitoring system designed by Medilogic | The sensor was placed on one foot between the soles and socks of the participants | 10-m walking test recorded at 300 Hz sampling | Peak pressure was recorded in each case by dividing the foot into seven segments and then comparing the pressure distribution of each region in each of the two classes |
Corpin and Ryan Rey A. [60] | 36 | N/A | Tekscan Medical Sensor 3000E | Single Tekscan Medical sensor placed on the right foot only. | 7 m walking in a straight line and repeat the procedure eight times | In-shoe pressure monitoring system was used. The Tekscan software provides a number of gait and pressure parameters that can be used as features for ML algorithms |
Wang et al. [61] | 20 | DPN: 5 H: 5 | Insole piezoresistive pressure sensor array | Two insole pressure sensors that each contained eight pressure measuring points were placed on both feet. | 20-m walking test | Using the proposed insole system, the pressure data were collected from each sensing point, and peak pressures were recorded to create a database of healthy and unhealthy subjects. Five different classification algorithms were then trained for the diagnosis, and the model was validated by using k-fold validation. |
Morshed et al. [65] | 20 | DPN: 10 H: 10 | Holter device | Four-channel (RA-LA, LA-LL, LL-RA, and Vx-RL) Holter device | 24-h ECG recording at 200 Hz | HRV parameters were extracted from ECG data using a method in [75]. Both time-domain and frequency-domain features of the ECG signal were used in the diagnosis of PN. |
Jelinek et al. [68] | 21 | DPN: 21 | ECG | ECG sensor with lead II configuration | 20-min ECG recording in spine position | Using HRV attributes of the ECG signal, a new multi-level clustering technique was proposed and implemented to distinguish between two classes. |
Sharanya, S. and P.A. Sridhar [73] | 19 | CAN: 9 H: 10 | ECG | ECG sensor with lead II configuration | 20-min ECG recording | A CNN network was used to distinguish between PN and healthy subjects. A 20-min-long ECG was recorded for each subject. |
References | No. of Participants | Distribution of Participants | No. of Sensors and Placement | Data Collection Procedure | Methodology | Results |
---|---|---|---|---|---|---|
Sejdic et al. [78] | 35 | DPN: 11 PD: 10 H: 14 | Accelerometer (ACC) and 3D optical motion capture system (Natural Point, Inc., Corvallis, OR, USA) | Single ACC attached at the torso | 3 m walking test | Accelerometer and 3D optical system captured the gait data at 100 Hz. Different spatiotemporal and frequency domain features were extracted. |
Khandakar et al. [80] | 12 | N/A | Force-sensitive resistors and temperature sensors based on thermistor | Two in-shoe wireless pressure and temperature monitoring systems using 16 FSR and 8 temperature sensors for each foot. | 20 m walking test at a sampling rate of 40 Hz | NodeMCU and multiplexer were used to send all the data wirelessly to the main computer. |
Kukreja et al. [79,81] | N/A | N/A | FlexForce Sensor and two DHT11 temperature sensors | In-shoe flexi pressure sensor was placed in the sole of the shoe, and two temperature sensors were aligned parallel to the instep and sole. | The data were collected from the participant wirelessly using NodeMCU and in-shoe sensors | Threshold values for the discrimination between healthy and PN patients were evaluated and used to diagnose PN |
Sempere-Bigorra et al. [82] | DPN: 8 Diabetic: 77 | IMU, vibration test, and sensitivity test | IMU attached to a lumbar area on the L5 spinal segment | The gait data were collected by using an IMU sensor, and data from other tests such as vibratory and sensitivity tests were also collected | The data were analyzed in order to find a correlation between these data. Logistic regression was then used to find the similarities between different groups. | |
Z. Veličković et al. [86] | PN: 11 Healthy: 12 | NCS and four IMUs | NCS electrodes were placed at the chest, and IMUs were attached to each leg and arm | First, NCS data were collected for all participants, and then six different exercises were performed to record and process IMU data | The obtained results show that wearable devices can be made useful in the diagnosis of PN. The overall results showed good specificity and sensitivity. |
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Talha, M.; Kyrarini, M.; Buriro, E.A. A Survey of the Diagnosis of Peripheral Neuropathy Using Intelligent and Wearable Systems. Technologies 2023, 11, 163. https://doi.org/10.3390/technologies11060163
Talha M, Kyrarini M, Buriro EA. A Survey of the Diagnosis of Peripheral Neuropathy Using Intelligent and Wearable Systems. Technologies. 2023; 11(6):163. https://doi.org/10.3390/technologies11060163
Chicago/Turabian StyleTalha, Muhammad, Maria Kyrarini, and Ehsan Ali Buriro. 2023. "A Survey of the Diagnosis of Peripheral Neuropathy Using Intelligent and Wearable Systems" Technologies 11, no. 6: 163. https://doi.org/10.3390/technologies11060163