1. Determining Level of Postural Control in Young Adults Using Force-Sensing Resistors Student ID ： M9920103 Student ： Kun-Hong Lee Adviser ： Ming-Yuan Shieh Alpha Agape Gopalai, Member, IEEE, S. M. N. Arosha Senanayake, Senior Member, IEEE, and Darwin Gouwanda, Member, IEEE PPT 製作率 :100% 1

2. Outline ABSTRACT INTRODUCTION SYSTEM HARDWARE DESIGN AND IMPLEMENTATION OF THE SYSTEM EXPERIMENTAL RESULTS CONCLUSION REFERENCES 2

3. ABSTRACT A force-sensing platform (FSP), sensitive to changes of the postural control system was designed. The platform measured effects of postural perturbations in static and dynamic conditions. This paper describes the implementation of an FSP using force-sensing resistors as sensing elements The objective of this research was to establish the feasibility of using an FSP to test and gauge human postural control. 3

4. INTRODUCTION The aim of this investigation was to study the feasibility of using the force-sensing platform (FSP) to assess stability. The designed system provided observers qualitative (real time) and quantitative (post acquisition) measures of subjects’ proprioceptive response and postural control. Potential application of the platform is in rehabilitation. 4

5. INTRODUCTION Recuperating athletes can gauge postural control before resuming formal training. Non sporting individuals can use the platform to gauge, monitor, and train proprioception control. Good proprioceptive control results in lower risk of balance and foot-related injuries. The obtained results demonstrated the ability of the system as a viable solution to postural analysis in clinical therapeutic applications. 5

6. SYSTEM HARDWARE A. Force-Sensing Platform The constructed platform utilized 122 FSRs with a sensing area of 126.68 mm2 per sensor, placed 40-mm apart (measured from the center of each sensing element). The changes detected in terms of resistance were converted into voltages linearly, utilizing signal conditioning circuitry. 6

7. SYSTEM HARDWARE A. Force-Sensing Platform The platform was designed to ensure the largest possible instrumentation area, allowing subjects to place their foot naturally on the platform. Natural foot placement was important to ensure that the readings acquired reflect pathological conditions of subjects 7

8. SYSTEM HARDWARE B. Signal Conditioning, Circuit Switching, and Data Acquisition Each FSRs output was connected to a noninverting operational amplifier for the amplification of signal strength. The amplifiers were divided into four quadrants based on its position on the board. Each quadrant was controlled by a multiplexer. These multiplexers switched channels between operational amplifiers. 8

9. SYSTEM HARDWARE B. Signal Conditioning, Circuit Switching, and Data Acquisition Fig. 1. (a) FSR. (b) Overall architecture of the signal conditioning and switching circuit implemented, describing the interface between host computer and signal acquisition. 9

10. SYSTEM HARDWARE C. BOSU Balance Training Platform Fig. 2. (a)–(d) Technical drawing of the BOSU balance trainer [16]. (e) FSP mounted on the balance trainer for acquisition of readings in perturbed conditions. (f) Subject mounted on the BOSU balance trainer that has been fitted with the FSP and use of hand rails for additional safety support in dynamic condition. 10

11. DESIGN AND IMPLEMENTATION OF THE SYSTEM Fig. 3. Flow chart describing the force-sensing system’s functionality. Analog signals are acquired by the FSRs on the FSP and digitized before making the data available for real-time/ postacquisition analysis. 11

12. DESIGN AND IMPLEMENTATION OF THE SYSTEM A. Real-Time Visual Representation of Pressure Profile The designed system presented visual feedback to end users in real time. Real-time visual data representation was useful in providing qualitative assessment of subjects’ postural control and pressure points. 12

13. DESIGN AND IMPLEMENTATION OF THE SYSTEM A. Real-Time Visual Representation of Pressure Profile A rainbow color scale was used to represent low force intensities with colors close to black (cold colors) and high force intensities with colors close to white (hot colors). This form of real-time feedback to end-users [see Fig. 4(a)] eases the process of identifying regions with high force concentrations at the foot. 13

14. DESIGN AND IMPLEMENTATION OF THE SYSTEM A. Real-Time Visual Representation of Pressure Profile Fig. 4. (a) Snapshot of the IGUI feedback displaying real-time captured foot patterns with varying color intensities to represent regions of high force concentrations. (b) Reproduced foot boundary from reduced data set; migration of weighted center of the applied pressure over time was superimposed on the traced foot boundaries. 14

15. DESIGN AND IMPLEMENTATION OF THE SYSTEM B. Off-Line Representation of Pressure Concentration Sites Stored signals were retrieved for analysis using MATLAB R2009b and checked for redundancy. Redundancy in the data sets referred to regions of the FSP that did not come in contact with the subjects’ foot. 15

16. DESIGN AND IMPLEMENTATION OF THE SYSTEM B. Off-Line Representation of Pressure Concentration Sites 16

17. DESIGN AND IMPLEMENTATION OF THE SYSTEM B. Off-Line Representation of Pressure Concentration Sites 17

18. DESIGN AND IMPLEMENTATION OF THE SYSTEM B. Off-Line Representation of Pressure Concentration Sites The X plane on the platform corresponds to the ML anatomical plane, while the Z plane refers to the AP anatomical plane. Fig. 5. Sectioned view of the FSP, depicting the arrangement of four FSRs, and the magnitude and location of the resultant force Fy, when FSRs are pressed. 18

19. DESIGN AND IMPLEMENTATION OF THE SYSTEM C. Fuzzy Clustering The FCM algorithm was used in this paper to identify 2- D regions with repeating locations of data concentration (pressure concentration) along the ML and AP planes. The objective function used was the distance from any given data point to a cluster center, weighted by that data point’s membership grade. 19

20. EXPERIMENTAL RESULTS A. Real-Time Qualitative Assessment Fig. 6. Real-time view of the IGUI provided to the end-user. The IGUI allows for the rainbow color scale graph to be rotated, and viewed as a 3-D graph for ease of visualization (a) Default acquisition view: EO. (b) Default acquisition view: EC. (c) Rotated acquisition view: EO. (d) Rotated acquisition view: EC. 20

21. EXPERIMENTAL RESULTS B. Pressure Concentration Sites Fig. 7. Single representative subject’s zoomed-in view on the distribution of COP plotted on a coordinate scale in units (1 unit = 40 mm). (a) Left foot. (b) Right foot. 21

22. EXPERIMENTAL RESULTS C. Clustering Distribution Sites Fig. 8. Fuzzy clustering applied to the calculated pressure distribution coordinates to identify occurrences of clusters within data set in EO state while subject is on static surface. (a) Left foot (b). Right foot. Identified clusters are marked as black dots on the graph. 22

23. EXPERIMENTAL RESULTS D. Lifestyle and Postural Control Fig. 9. Average distribution area of COP of all subjects sorted from “active” to “inactive” lifestyle along the x-axis. (a) Static condition, EO. (b) Static condition, EC. (c) Dynamic condition, EO. (d) Dynamic condition, EC. 23

24. CONCLUSION The results obtained demonstrated the sensitivity of the developed system toward changes made in the postural control system. Individual with or without balance disorder could benefit by relying on the measurements of the system to identify, correct, monitor, and suggest improvements for problems with the proprioception response of the feet, which influences the postural control and occurrences of foot related injuries. 24

25. Tthank you for your attention 25