| Name of the Author |
Techniques to monitor PD |
Results |
| Ene[1] |
Probabilistic Neural Network (PNN), and search methods such as Monte Carlo Search (MCS), Incremental Search (IS) and Hybrid Search (HS) |
Accuracy between 79% and 81% |
| Kostek et al. [2] |
Rule based decision algorithm using the methodology UPRDS |
Rough set based decision algorithm is most suitable |
| Tian et al. [3] |
Weighted fuzzy membership functions |
higher accuracy of 92.82%, specificity 85.42% and performance sensitivity 95.24% |
| Gil and Johnson [4] |
Support vector machines (SVMs) and artificial neural networks (ANNs) |
90% accuracy |
| Iris Tienet al. [5] |
Wireless sensor based |
They placed the wireless sensor based sensors at different places of the shoe and noted the movement disorders |
| Lones et al. [6] |
Evolutionary algorithms |
95% accuracy |
| Pavel et al. [7] |
Algorithms based on Hidden Markov Model (HMM) |
Ability to assess gait velocity and its Variability |
| Fotiadis et al. [14] |
Wearable devices |
Visualize the vital signs of patients |
| El-Gohary et al. [8] |
Internal sensors |
Ability to track tremor in PD patients |
| Chen et al. [9] |
Mercury Live, a web based application working in tandem with wearable devices |
Able to monitor PD |
| Sant’Anna et al. [10] |
Introduced symbol based symmetry index in the early detection of Parkinson’s disease |
High specificity and sensitivity |
| Lorincz et al. [11] |
Sensor network platform |
Monitor the patients’ fall-like motions and discriminates them from actual falls |
| Lorincz et al. [11] |
Wearable devices |
Motion analysis of patients |
| Manto et al. [12] |
Wearable devices |
Helped in suppression of upper-limb tremor |
| Darwish and Hassanien[13] |
Wearable and implantable body sensor networks (WIBSN) |
More accuracy is achieved with inter-disciplinary sciences |
| Mario PANSRA et al. [26] |
SHIMMER PLOTFORM |
Results were collected on shimmer platform and Processed the accuracy they got is about 85% |
| Ron sengchang et al. [27] |
Laser Lines and CMOS Sensor |
Using LASER lines they calculated the resting tremor and the sensor is placed in shoe to identify the movement disorder The accuracy they got is about 90% |
| Bustamante bello et al. [29] |
Gloves with Accelerometers |
Accelerometers Embedded in Gloves to identify the resting tremor |
| ERocon et al. [30] |
Robot based Wearable sensor(WOTAS) |
They fixed the accelerometer sensors at different places of the hand and observed the tremor of the hand. |
| Barth j et al.[31] |
Sensor based Pen |
By the variation in the hand writing, which was observed by sensor based pen |
| Ming Fing Chang [28] |
CAIROW (Context aware assisted interactive Robotic Walker) |
The severity of Parkinson’s disease can be identified by walk test |
| M. Elsayed[32] |
Ubiquitous Health care Monitoring |
By the impairment of walking gait |
| Shamalpatel et al. |
Support vector Machne |
By the sensor data they calculated the window length and checked the severity of the Parkinson’s Disease. |
| Ahmed AI jawed [33] |
TUG – TIME UP AND GO |
Walk test to predict parkinson’s Disease |
| Mark V et al. [35] |
Determining posture from physiological tremor |
The accuracy is about 75-80% |
| References [15-25] |
Speech based solutions |
Found relation between PD and the change in phonetics of patients |
