Journal: Sensors (Basel, Switzerland)

Article Title: Usage of Machine Learning Techniques to Classify and Predict the Performance of Force Sensing Resistors

doi: 10.3390/s24206592

Figure Lengend Snippet: Metric comparison between Interlink and Peratech sensors.

Article Snippet: In order to obtain statistically representative results, the experimental tests in this study were performed on 48 Interlink FSR402 sensors manufactured by Interlink Electronics (Irvine, CA, USA) [ ] and on 48 QTC Peratech SP200 sensors manufactured by Peratech Holdco Limited (Sedgefield, UK) [ ].

Techniques: Comparison

Journal: Sensors (Basel, Switzerland)

Article Title: Usage of Machine Learning Techniques to Classify and Predict the Performance of Force Sensing Resistors

doi: 10.3390/s24206592

Figure Lengend Snippet: Photographs of the mechanical setup. ( a ) Overview of the testbench; ( b ) Zoom-in photo depicting the linear motor for applying forces to the bunch of sensors. ( c ) Photograph of FSRs installed inside sensor holders, Peratech SP200 (yellow) and Interlink FSR402 (orange). ( d ) Zoom-in photo depicting the sensors inside the chamber and the spring for mechanical compliance. ( e ) Photograph of two side-by-side sensor holders showing the puck (top side) and the notch (bottom side). ( f ) Custom design element for holding aligned the sensor holders, and the spring. A FSR was placed near the element for comparison purposes.

Article Snippet: In order to obtain statistically representative results, the experimental tests in this study were performed on 48 Interlink FSR402 sensors manufactured by Interlink Electronics (Irvine, CA, USA) [ ] and on 48 QTC Peratech SP200 sensors manufactured by Peratech Holdco Limited (Sedgefield, UK) [ ].

Techniques: Comparison

Journal: Sensors (Basel, Switzerland)

Article Title: Usage of Machine Learning Techniques to Classify and Predict the Performance of Force Sensing Resistors

doi: 10.3390/s24206592

Figure Lengend Snippet: Drift error ( d.e. ) at multiple input voltages for the Interlink FSR402 ( a – c ) and QTC Peratech SP200 ( d – f ) sensors.

Article Snippet: In order to obtain statistically representative results, the experimental tests in this study were performed on 48 Interlink FSR402 sensors manufactured by Interlink Electronics (Irvine, CA, USA) [ ] and on 48 QTC Peratech SP200 sensors manufactured by Peratech Holdco Limited (Sedgefield, UK) [ ].

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Usage of Machine Learning Techniques to Classify and Predict the Performance of Force Sensing Resistors

doi: 10.3390/s24206592

Figure Lengend Snippet: Pearson Correlation Coefficient (PCC) for the drift error as a function of V o_null at multiple input voltages.

Article Snippet: In order to obtain statistically representative results, the experimental tests in this study were performed on 48 Interlink FSR402 sensors manufactured by Interlink Electronics (Irvine, CA, USA) [ ] and on 48 QTC Peratech SP200 sensors manufactured by Peratech Holdco Limited (Sedgefield, UK) [ ].

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Usage of Machine Learning Techniques to Classify and Predict the Performance of Force Sensing Resistors

doi: 10.3390/s24206592

Figure Lengend Snippet: Drift error ( d.e. ) measured at V i = 5 V with three superimposed trendlines. ( a ) Interlink FSR402, ( b ) QTC Peratech SP200.

Article Snippet: In order to obtain statistically representative results, the experimental tests in this study were performed on 48 Interlink FSR402 sensors manufactured by Interlink Electronics (Irvine, CA, USA) [ ] and on 48 QTC Peratech SP200 sensors manufactured by Peratech Holdco Limited (Sedgefield, UK) [ ].

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Usage of Machine Learning Techniques to Classify and Predict the Performance of Force Sensing Resistors

doi: 10.3390/s24206592

Figure Lengend Snippet: Fit parameters for the drift error ( d.e. ) models at V i = 5 V.

Article Snippet: In order to obtain statistically representative results, the experimental tests in this study were performed on 48 Interlink FSR402 sensors manufactured by Interlink Electronics (Irvine, CA, USA) [ ] and on 48 QTC Peratech SP200 sensors manufactured by Peratech Holdco Limited (Sedgefield, UK) [ ].

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Usage of Machine Learning Techniques to Classify and Predict the Performance of Force Sensing Resistors

doi: 10.3390/s24206592

Figure Lengend Snippet: Hysteresis error ( h.e. ) measured at multiple input voltage for Interlink FSR402 ( a – c ) and QTC Peratech SP200 ( d – f ) sensors.

Article Snippet: In order to obtain statistically representative results, the experimental tests in this study were performed on 48 Interlink FSR402 sensors manufactured by Interlink Electronics (Irvine, CA, USA) [ ] and on 48 QTC Peratech SP200 sensors manufactured by Peratech Holdco Limited (Sedgefield, UK) [ ].

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Usage of Machine Learning Techniques to Classify and Predict the Performance of Force Sensing Resistors

doi: 10.3390/s24206592

Figure Lengend Snippet: Result of k -means clustering method using k = 2 for Peratech SP200 sensors at V i = 5 V.

Article Snippet: In order to obtain statistically representative results, the experimental tests in this study were performed on 48 Interlink FSR402 sensors manufactured by Interlink Electronics (Irvine, CA, USA) [ ] and on 48 QTC Peratech SP200 sensors manufactured by Peratech Holdco Limited (Sedgefield, UK) [ ].

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Usage of Machine Learning Techniques to Classify and Predict the Performance of Force Sensing Resistors

doi: 10.3390/s24206592

Figure Lengend Snippet: Result of k -means clustering method using k = 3 for Peratech SP200 sensors at V i = 5 V.

Article Snippet: In order to obtain statistically representative results, the experimental tests in this study were performed on 48 Interlink FSR402 sensors manufactured by Interlink Electronics (Irvine, CA, USA) [ ] and on 48 QTC Peratech SP200 sensors manufactured by Peratech Holdco Limited (Sedgefield, UK) [ ].

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Usage of Machine Learning Techniques to Classify and Predict the Performance of Force Sensing Resistors

doi: 10.3390/s24206592

Figure Lengend Snippet: Result of k -means clustering method using k = 4 for Peratech SP200 sensors at V i = 5 V.

Article Snippet: In order to obtain statistically representative results, the experimental tests in this study were performed on 48 Interlink FSR402 sensors manufactured by Interlink Electronics (Irvine, CA, USA) [ ] and on 48 QTC Peratech SP200 sensors manufactured by Peratech Holdco Limited (Sedgefield, UK) [ ].

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Usage of Machine Learning Techniques to Classify and Predict the Performance of Force Sensing Resistors

doi: 10.3390/s24206592

Figure Lengend Snippet: Flowchart summarizing the process for measuring and assessing the hysteresis error in Peratech SP200 sensors.

Article Snippet: In order to obtain statistically representative results, the experimental tests in this study were performed on 48 Interlink FSR402 sensors manufactured by Interlink Electronics (Irvine, CA, USA) [ ] and on 48 QTC Peratech SP200 sensors manufactured by Peratech Holdco Limited (Sedgefield, UK) [ ].

Techniques: