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Record W2350753849 · doi:10.1115/1.4033221

Development of a Force Sensor Prototype for Medical Devices1

2016· article· en· W2350753849 on OpenAlexaffabout
Yu Hui Feng, Goldie Nejat

Bibliographic record

VenueJournal of Medical Devices · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStrain gaugeStiffnessPiezoelectricityComputer scienceTactile sensorLoad cellAssistive deviceContact forceSimulationHaptic technologyAcousticsEngineeringRobotElectrical engineeringStructural engineeringArtificial intelligencePhysical medicine and rehabilitationPhysicsMedicine

Abstract

fetched live from OpenAlex

The effectiveness of medical and assistive devices can be increased through the integration of sensory-based intelligence capabilities. Force sensors can be used within robotic grippers to directly provide force feedback during surgery [1]. They can also be embedded in prosthetics, clothes, or furniture to support the everyday tasks of patients. For example, they can be used to improve/regain gait and mobility by being placed inside the shoe of a person who is suffering from muscle and nerve damage [2] or into a treatment couch to measure forces applied during posteroanterior mobilization to the lumbar spine [3]. In addition, the force sensors can be implemented in prosthetic knees as sensory feedback [4] in order to assist transfemoral amputees to walk and run more naturally. Various types of sensors including load cells [3], strain gauges [1], and piezoelectric force sensors [4] have been utilized. In general, the load cells are typically bulky in size and the strain gauges need significant deformation in order to be able to measure forces. On the other hand, the piezoelectric force sensors are able to provide high-resolution force measurements within a compact size [5]. In addition, the high stiffness of their sensing elements results in high natural frequencies, and thus provides a wide operational frequency bandwidth to measure dynamic force [5]. In this paper, we present the prototype development and calibration of a three-axis piezoelectric force sensor. Due to its novel features, the sensor can be used in various medical and assistive device applications.The piezoelectric force sensor we have developed is presented in Fig. 1. The sensor uniquely utilizes three groups of two piezoelectric sensing elements made of BM 800 piezoelectric ceramics [6]. The two sensing elements in each group have the same polarization direction and are utilized to measure forces in the normal, and two shear directions, Fig. 2. The sensing groups are compressed between a top and a bottom plate with four preloading screws. The overall size of the sensor prototype is 24.25 mm × 24.25 mm × 9.25 mm. Copper shims with tabs, Fig. 2, are used to connect the sensing elements to the sensor amplification system.Figure 3 provides an exploded view of the sensor prototype. The sensor groups are stacked on the bottom plate and compressed with the top plate with the four preloading screws. A side housing is fixed onto the bottom plate for the protection of the sensing elements. All the sensor components are made of stainless steel A2.The amplification system of the force sensor consists of the amplification circuit for each force measurement direction and the data acquisition device. The amplification circuit includes a preamplifier (Amptek A250) that converts the charge signal received from the elements to a voltage signal and a postamplifier (Amptek A275) that further amplifies the input signal while preserving the linear information between the input and output voltage. The design of the amplification circuit is shown in Fig. 4.To verify the performance of the force sensor prototype in terms of its resolution, sensing range, and operational frequency bandwidth, dynamic calibration experiments were conducted. A piezoelectric actuator (NEC/TOKIN AE0203D04F) was used to provide the input force signal. An oscillating driving voltage was utilized for the actuator. The frequency of the input force was determined by the frequency of the driving voltage, where the amplitude of the input force is linearly proportional to that of the driving voltage. To control the driving voltage, a function generator (WAVETEK 164) was utilized. A single-axis calibration sensor (Kistler 9712B5) was used to verify the relationship between the amplitude of the driving voltage and the amplitude of the force output from the actuator during a precalibration stage.Dynamic calibration of each axis was performed using a probe exerting forces onto the top plate of the force sensor prototype. The sensor prototype was secured to a reconfigurable fixture that aligned the corresponding axis of the sensor to the direction of the applied force. Figure 5 provides an overview of the prototype sensor calibration setup for all three directions.The calibration experiments consisted of applying forces at frequencies ranging from 1 kHz to 8 kHz with an increment of 1 kHz. Then, the force measurements were compared to the forces determined during the precalibration stage. The root-mean-square error (RMSE) was determined as the minimum measurable force for each frequency level. The largest RMSE throughout the frequency range was then defined as the resolution of the sensor for each axis. This was determined to be 3 mN for all three axes. In addition, the sensing range was determined to be up to 10 N. Figure 6 illustrates an example of the calibration results for the three axes with an input of 10 N at 8 kHz.The three-axis force sensor prototype was calibrated to have a high operational frequency bandwidth for dynamic force measurements of up to 8 kHz, a high resolution of 3 mN, and a sensing range of up to 10 N. The combination of the high-frequency bandwidth and high resolution are unique features of our force sensor as it provides both these advantages when compared to other types of piezoelectric force sensors used in medical applications (e.g., Refs. [4] and [7]). By scaling the size of the sensor, it can be used for different frequency bandwidth and force sensing requirements. Our future work consists of investigating cross-axis sensitivity and the physical integration of our force sensor into the end-effector of a surgical robot operating at a high control frequency to provide force feedback during surgical operations.This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canada Research Chairs (CRC) Program.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.791
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.283
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2016
Admission routes2
Has abstractyes

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