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REVIEW 4 major objections 5 minor 2 cited by

Design and Implementation of an IoT-based Respiratory Motion Sensor

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The paper claims a compact FSR-based wearable can stream respiratory motion in real time over BLE 5 at 0.4 mW.

desk verdict A clean hardware write-up with zero validation data; the central sensing claim is asserted, not shown. read the letter →

arxiv 2412.05405 v1 pith:6NNGRVKY submitted 2024-12-06 eess.SP

Bardia Baraeinejad , Maryam Forouzesh , Saba Babaei , Yasin Naghshbandi , Yasaman Torabi , Shabnam Fazliani This is my paper · ORCID
classification eess.SP
keywords respiratoryratemonitoringForceSensingResistor(FSR)wearablesensorBluetoothLowEnergy5IoThealthnon-invasiverespirationmeasurementchestcircumferencelow-powermedicaldevice
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that a small, PCB-based wearable with a Force Sensing Resistor can monitor a patient's respiratory movements in real time by converting breathing-driven chest expansion into a measurable resistance change. The device reads that resistance with a voltage divider and a 12-bit ADC, streams the signal to a smartphone over BLE 5, and runs from a 450 mAh LiPo battery at a claimed 0.4 mW. If the central claim holds, it offers a low-cost, comfortable, non-invasive alternative for continuous respiratory-rate monitoring in home and clinical settings. The paper's case rests on component selection and a schematic-level argument rather than on measured validation data.

What carries the argument

The load-bearing mechanism is a voltage divider built from a fixed 499 kΩ resistor in series with the FSR: $V_{\mathrm{out}} = V_{\mathrm{dd}} \times \frac{R}{R + R_{\mathrm{FSR}}}$. Chest expansion presses the FSR, its resistance drops, and the divider output rises, which a 12-bit ADC samples on its second channel. The paper leans on the FSR's large resistance swing under small forces to make respiratory motion visible, and on the low-quiescent-current buck converter (TPS62840) and BLE 5 radio to make the streaming low-power.

What would settle it

Strap the device to a subject who breathes normally for two minutes and then holds their breath while shifting posture; if the recorded trace does not flatline during the breath hold and instead follows the posture shifts, the claim that the FSR reads respiration specifically is falsified. A stronger test is to record the FSR alongside a reference chest-circumference belt during graded breaths and check whether the FSR signal tracks the belt with consistent sensitivity and without motion-artifact contamination.

Watch

Extended reading notes

Core claim

The paper's central claim is that a single FSR embedded in a small PCB-based wearable can convert the mechanical strain of chest expansion during breathing into a resistance change, which, read through a voltage divider and a 12-bit ADC, yields a real-time respiratory motion signal. The device streams this signal over BLE 5 to a smartphone, includes a 3-axis accelerometer for detecting body motion, and is powered by a rechargeable LiPo battery through a low-quiescent-current buck converter, giving a power consumption of 400 µW. The authors assert that this configuration constitutes a non-invasive, comfortable, long-term respiratory monitor that is easy for patients to wear.

Load-bearing premise

The paper assumes, without calibration, that breathing-driven chest expansion presses the FSR hard enough, in its rigid PCB mounting and as worn on the body, to produce a resistance signal that is clearly respiratory rather than ordinary body movement.

Editorial extensions

If this is right

  • The device could provide real-time respiratory motion traces and breathing rate on a smartphone without belts or skin electrodes, improving comfort for long-term monitoring.
  • At the claimed 0.4 mW, a 450 mAh battery could sustain monitoring for an extended period, making continuous home monitoring practical for sleep apnea or post-operative respiratory depression.
  • Combining FSR chest-motion sensing with a 3-axis accelerometer could allow motion artifact to be identified or subtracted, yielding cleaner respiratory signals during daily activity.
  • The same voltage-divider and 12-bit ADC readout chain could be adapted to other body locations or to different force-sensing materials, extending the design to abdominal breathing or posture-related pressure changes.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • We infer that if the FSR signal is validated against a reference respiration belt, the same hardware could likely estimate not just respiratory rate but relative tidal-volume changes, since chest circumference excursion correlates with inhaled volume.
  • The absence of calibration data points to a first testable extension: apply known forces to the FSR in its mounted configuration and map resistance to chest circumference, which would settle whether the 499 kΩ divider suits the FSR's actual force range.
  • We infer that the rigid-PCB mounting may make sensor placement on the chest wall rather than the abdomen critical, since a strap or adhesive mount that presses the FSR against the skin could turn small circumference changes into larger local forces.
  • A natural next step beyond the paper is a clinical comparison with an impedance pneumograph or capnograph to quantify the accuracy and latency of the claimed real-time respiratory signal.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper reports the design and implementation of a wearable IoT-based respiratory motion sensor. The device measures chest/abdomen circumference changes using a Force Sensing Resistor (FSR) attached to a printed circuit board, samples the FSR with an ADC on an nRF52832 MCU, and streams data over Bluetooth Low Energy 5. The authors claim low power consumption, high precision (12-bit resolution), and long battery life. The manuscript includes a hardware block diagram, a firmware flowchart, component choices, a brief sensor comparison table, and a single unlabeled output trace. There is no results section and no experimental validation against a reference respiration measurement.

Significance. If the central claim were demonstrated, a compact, low-power, BLE-enabled FSR-based respiration monitor could be a useful addition to wearable pulmonary monitoring, particularly for comfort and long-term use. The paper's positive aspects are its clear identification of the application, the component-level hardware description, and the attention to power management via a low-IQ buck converter. However, the paper currently provides only a design description; the core functionality is asserted rather than measured. There are no machine-checked proofs, no reproducible code or datasets, and no quantitative evaluation, so the scientific contribution is conditional on validation that the manuscript does not supply.

major comments (4)
  1. [II-C and Fig. 1] The sensing principle is not established. The paper states that breathing-induced chest circumference changes create "mechanical strain" that changes the FSR resistance, but an FSR is a normal-force sensor, and the manuscript provides no mechanical model, no calibration data, and no characterization of how the FSR is coupled to the body. Without evidence that a few centimeters of chest expansion produce a detectable, respiration-specific force on the FSR as mounted on the PCB, the recorded signal in Fig. 6 cannot be attributed to respiration. This is a load-bearing gap in the central claim.
  2. [Fig. 6 and absence of a results section] There is no experimental evaluation section. The only signal trace, Fig. 6, has no time axis, no units, no scale, and no reference channel or ground-truth respiration signal. It is therefore impossible to verify that the device measures respiratory motion, that the trace period corresponds to breathing, or that the signal is distinguishable from motion artifact or body pressure changes. The central claim of "real-time monitoring of respiratory system movements" rests entirely on this unvalidated trace.
  3. [Abstract and Section I] The power consumption figures are contradictory: the abstract states 0.4 mW (400 µW), while the introduction states 4.9 mW. These differ by more than an order of magnitude and no measurement of power consumption is reported. Because the paper claims low power and long battery life, this inconsistency undermines a secondary quantitative claim and must be resolved with measured data.
  4. [Section IV (Conclusion)] The conclusion claims "high resolution of 12 bits" for capturing respiratory signals. This is the resolution of the ADC, not a measurement of the device's accuracy, precision, or signal-to-noise ratio for respiratory motion. No data on repeatability, sensitivity, or comparison with a reference respiration monitor is provided, so the claim of high precision is unsupported.
minor comments (5)
  1. [Abstract and Section I] The power figure in the abstract (0.4 mW) and in Section I (4.9 mW) should be reconciled; the abstract also uses both "0.4 mW" and "400 µW," which are equal but could confuse readers.
  2. [Throughout] The MCU name is spelled inconsistently as "nRf52832" and "nRF52832"; use the manufacturer's capitalization consistently.
  3. [Reference [11]] Reference [11] is incomplete, lacking the document title, author, and publication details for the LIS2DH12 accelerometer datasheet.
  4. [Section III] The sentence "FSR technology has overload cells and strain gauges" is unclear; it appears to mean "over load cells and strain gauges," but the wording should be corrected.
  5. [Fig. 6] Figure 6 is not referenced in the text, and its caption "Illustrating the Signal During Device Usage" does not identify what is plotted, the subject, or the recording conditions.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular reasoning found; the paper's central weakness is missing experimental validation, not derivation-from-inputs.

full rationale

The paper contains no derived predictions with fitted parameters, and no load-bearing claim is justified by self-citation. The only quantitative relationship is Eq. (1), the standard voltage-divider formula V_out = V_dd * R/(R + R_FSR), which follows from Ohm's law and maps FSR resistance to output voltage rather than predicting any independently measured quantity. The central claim that chest expansion produces a measurable FSR resistance change is an untested assumption introduced in Section II-C and illustrated without calibration in Fig. 6; that is an evidence gap or a correctness risk, not a circular reduction. The self-citations [3] and [7] appear only as contextual literature references in the introduction and do not carry the load-bearing sensing claim, and there is no uniqueness theorem, ansatz-smuggling citation, or renaming of a known result. Consequently, the derivation chain contains no step that is equivalent to its own inputs by construction, so the circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no free parameters and no invented entities. Its central claim relies on domain assumptions about FSR behavior and mechanical coupling that are plausible but unvalidated, plus a standard voltage divider equation. The absence of fitted parameters means the circularity burden is low, but the absence of validation data makes the correctness risk high.

assumptions (3)
  • domain assumption FSR electrical resistance decreases monotonically with applied force, so chest expansion produces a measurable resistance change.
    This is the core sensing principle invoked in Section II-C, but the paper provides no calibration or mechanical data showing the FSR response under the specific mounting and body placement.
  • domain assumption Breathing-induced strain on the PCB-mounted FSR is large enough to be distinguished from motion artifacts and noise.
    The paper assumes this in Section II-C and Fig. 1, yet provides no signal-to-noise analysis, no artifact rejection, and no comparison with a reference respiration measurement.
  • standard math The voltage divider equation Vout = Vdd * R / (R + R_FSR) accurately captures the FSR readout with the chosen fixed resistor.
    This is a standard circuit equation, but the choice of 499 kohm is not justified, and the FSR's exact resistance range and loading effects are not characterized.

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Cite this review

Pith. "Pith review of Design and Implementation of an IoT-based Respiratory Motion Sensor." pith.science (2026). https://pith.science/paper/6NNGRVKY

@misc{pith2026241205405,
  author       = {Pith},
  title        = {Pith review of: Design and Implementation of an IoT-based Respiratory Motion Sensor},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6NNGRVKY}},
  note         = {Machine review of arXiv:2412.05405}
}
read the original abstract

In the last few decades, several wearable devices have been designed to monitor respiration rate in an effort to capture pulmonary signals with higher accuracy and reduce patients' discomfort during use. In this article, we present the design and implementation of a device for real-time monitoring of respiratory system movements. When breathing, the circumference of the abdomen and thorax changes; therefore, we used a Force Sensing Resistor (FSR) attached to the Printed Circuit Board (PCB) to measure this variation as the patient inhales and exhales. The mechanical strain this causes changes the FSR electrical resistance accordingly. Also, for streaming this variable resistance on an Internet of Things (IoT) platform, Bluetooth Low Energy (BLE) 5 is utilized due to the adequate throughput, high accessibility, and possibility of power consumption reduction. Furthermore, this device presents features such as low power consumption (0.4 mW), high precision, and ease of use.

Figures

Figures reproduced from arXiv: 2412.05405 by the authors.

Figure 4
Figure 4. PCB of the respiration device G. Body Design The enclosure of this device is manufactured by Stereolithography (SLA) resin printing according to its high accuracy, isotropic, high thermal durability, and high finish surface [20]. The material used for printing is ABS-like (Acrylonitrile Butadiene Styrene) resin. Although it might be a little more expensive than standard resin, it has more impact resistance, tensile … view at source ↗
Figure 5
Figure 5. Final Body Design [PITH_FULL_IMAGE:figures/full_fig_p003_5.png] view at source ↗
Figure 6
Figure 6. Illustrating the Signal During Device Usage Advantages Limitations FSR Small size, low cost, good shock resistance and linear transfer function for small forces, high durability, no battery usage, capable of measuring a wide range of forces because they can be manufactured in different shapes and force ranges[26] [27] [28] Non-negligible hysteresis for high force values [28] Strain Gauge Small size, multi-axis measu… view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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Reference graph

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.