The challenge the framework addresses is well documented in biomedical engineering education. Core technical courses have long relied on didactic lectures, leaving students to absorb physiological measurement theory, amplifier design, and signal processing without frequent opportunities to apply those concepts in authentic contexts. Experiential learning, when it exists, is typically deferred to the final year, and capstone or clinical immersion experiences are not universally required. That delay can weaken student engagement, slow the formation of professional identity, and leave graduates underprepared for the communication demands of engineering practice. Prior surveys of biomedical engineering classrooms have identified persistent barriers to engagement, and large-scale studies of STEM teaching in North American universities have shown that lecture-dominated instruction remains the norm across many disciplines.

The redesigned course, developed by Ria Mazumder of the Department of Biomedical Engineering and the Center for Teaching and Learning at Widener University, integrates three interconnected interventions into a single scaffolded sequence. The first is a set of Arduino-based hands-on laboratories in which students work directly with physiological sensors, biosignal acquisition hardware, and microcontroller-controlled systems. Rather than treating the microcontroller platform as a toy demonstration, the course uses it as a genuine engineering tool: students build circuits that detect and record biological signals, program the acquisition pipeline, and interpret the resulting data with the same rigor expected of professional instrumentation work.

The second intervention, designated Phase 1, consists of community-engaged STEM workshops. Students take the concepts they have just mastered in the laboratory and translate them into accessible, hands-on demonstrations suitable for a non-technical youth audience. This translation step is not an afterthought but a deliberate pedagogical mechanism. Educational research on learning by teaching has shown that preparing to explain material to others produces measurable gains in the explainer’s own understanding, and the course exploits that effect by requiring students to distill amplifier theory, sensor physics, and signal processing into activities that children can grasp and enjoy.

The third intervention, Phase 2, escalates the challenge into team-based bioinstrumentation design projects implemented in partnership with a local Boys and Girls Club serving underrepresented youth. Student teams design, build, and refine instrumentation prototypes, then deliver them as interactive experiences for the club’s members. The partnership gives the engineering students an authentic client community with real constraints, while giving the young participants early exposure to engineering role models and hands-on science, a pairing the author argues supports both technical learning and civic development on both sides of the relationship.

Assessment of the redesigned course relied on descriptive data, including student surveys and written reflections. The results indicate strong engagement and perceived gains across technical, communication, and professional competencies mapped to the outcomes required by ABET, the accreditation body for engineering programs. Students reported that the service-learning structure helped them connect abstract theory to practice, sharpened their ability to communicate with non-technical audiences, and strengthened their sense of civic identity. These themes recurred across the reflection data, suggesting that the benefits were not confined to a handful of unusually motivated participants but represented a broad pattern in the cohort’s experience.

The theoretical grounding of the framework draws on several established strands of educational scholarship. Kolb’s experiential learning cycle positions concrete experience as the engine of learning and development, and the course operationalizes that cycle by moving students from laboratory experience to community application to structured reflection. Lave and Wenger’s model of situated learning frames expertise as legitimate participation in a community of practice, which the Boys and Girls Club partnership supplies in concrete form. The service-learning literature itself, including foundational work by Bringle and Hatcher and later distinctions between traditional and critical service-learning by Mitchell, informs the design’s emphasis on reciprocal community benefit rather than one-way outreach.

What distinguishes the framework from earlier service-learning efforts in engineering is its placement and its scaffolding. Service projects have historically been concentrated in capstone design courses, where they compete with the logistical pressures of a final-year project, or offered as standalone electives that reach only a subset of students. By embedding service-learning in a required junior-level core course, the model guarantees that every student encounters community-engaged engineering before the final year. The three-phase scaffold, moving from guided laboratories to workshops to open-ended design, gives students a graduated pathway into that experience, lowering the barrier that often discourages instructors from adding experiential components to technically dense courses.

The practical implications extend beyond Widener University. The author describes the framework as scalable and transferable, and the ingredients are deliberately modest: a low-cost microcontroller platform, a structured laboratory sequence, and a community partner willing to host student-led activities. The work was supported by external funding from a PECO Grant and a PEEP Grant awarded twice, along with internal Faculty Development and Course Mini-Grant support, suggesting that the resource requirements are within reach of many institutions. For programs seeking to satisfy ABET’s professional and societal competency outcomes without diluting technical content, the study offers a concrete template rather than an abstract aspiration.

The broader significance lies in what the model says about when professional formation should begin. If identity as an engineer, communicator, and civic participant crystallizes through repeated authentic practice, then deferring those experiences to the senior year forfeits years of development. The Widener experiment suggests that a core bioinstrumentation course, often considered one of the most technically demanding stops in the biomedical engineering curriculum, can simultaneously serve as a site of community engagement without sacrificing rigor. As biomedical engineering programs continue to grapple with engagement challenges and evolving accreditation expectations, the study offers evidence that the classroom and the community need not be competing priorities, but can be mutually reinforcing components of a single, well-scaffolded educational design.

Subject of Research: Integration of service-learning into a junior-level biomedical engineering bioinstrumentation course

Article Title: Embedding Service-Learning in a Core Junior-Level Bioinstrumentation Course: A Practical Framework for Integrating Technical Rigor with Professional and Societal Competencies

Article References: Embedding Service-Learning in a Core Junior-Level Bioinstrumentation Course: A Practical Framework for Integrating Technical Rigor with Professional and Societal Competencies. (n.d.). https://doi.org/10.1007/s43683-026-00250-9

Image Credits: AI Generated

DOI: 10.1007/s43683-026-00250-9

Keywords: service-learning, bioinstrumentation, biomedical engineering education, experiential learning, Arduino, community-engaged learning, ABET outcomes, STEM education, professional skills development, Widener University, Boys and Girls Club, curriculum design

The core innovation lies in what the device leaves out. Conventional automatic digital blood pressure monitors rely on a motorized air pump and a solenoid valve to inflate and deflate the cuff, two components that drive up manufacturing costs and power consumption. PressSure eliminates both. Instead, the user inflates the cuff manually with a hand bulb, and deflation is managed by a carefully calibrated controlled-leakage regulator that releases air at a steady rate of roughly 2.2 to 2.5 millimeters of mercury per second. That deflation speed is critical, because the oscillometric measurement technique the device uses depends on a slow, consistent pressure drop to capture the small pressure oscillations produced by arterial pulsation.

At the heart of the electronics is the MPS20N0040D-S pressure sensor, which outputs a tiny voltage between 2.3 and 30 millivolts in response to cuff pressure. A differential amplifier with a gain of 100 boosts this signal to a usable range of 0.23 to 3 volts. The signal is then split into two paths: a low-pass filter with a 10-hertz cutoff extracts the DC component representing absolute cuff pressure, while a high-pass filter with a 2-hertz cutoff isolates the AC component containing the pulsatile oscillations. Remarkably, all of the active filtering, amplification, and comparison is accomplished with a single LM324 quad operational amplifier chip, one of the cheapest integrated circuits on the market.

Signal processing is handled by an ATmega328P microcontroller, the same chip found in the Arduino Uno, preloaded with the Arduino bootloader so that anyone with a USB-to-serial adapter can reprogram it. The firmware implements an oscillometric algorithm that analyzes pulse amplitudes during deflation. As cuff pressure falls, oscillation amplitude rises to a maximum at the mean arterial pressure and then declines. The device estimates systolic pressure when the amplitude reaches 47 percent of the maximum on the rising side and diastolic pressure at 78 percent on the falling side, ratios the team empirically optimized against reference measurements and which fall squarely within ranges reported in the scientific literature.

One of the most sophisticated aspects of the design is its artifact rejection algorithm. Motion, speech, and muscle tension can all corrupt oscillometric pulse data, and the researchers found that disabling their rejection routine nearly doubled the mean absolute error for diastolic pressure, from 6.50 to 12.92 millimeters of mercury, and raised systolic error from 4.50 to 7.33 millimeters of mercury. The algorithm examines the timing between successive pulses and discards those with abnormal temporal characteristics before constructing the oscillometric envelope. Across representative measurements, it rejected an average of about 6 percent of detected pulses, keeping roughly 40 of the 43 to 50 pulses captured in each reading.

Power efficiency is another standout feature. The entire system draws approximately 50 milliamps, or about 300 milliwatts, during active measurement, and a 4N35 optocoupler completely disconnects the battery from the circuit during standby, reducing idle drain to less than a microampere. Four AA alkaline batteries can therefore power roughly 40 hours of continuous operation, equivalent to about 1,600 individual measurements. For clinics where electricity is unreliable and batteries are expensive, this ultra-low-power architecture is arguably as important as the device’s low price.

The enclosure demonstrates how far 3D printing has come as a manufacturing tool rather than merely a prototyping method. The body, cover, and button caps were designed in SolidWorks and printed in ABS plastic at 100 percent infill, chosen for its durability and heat resistance. A modular design philosophy means individual parts can be modified and reprinted without rebuilding the whole device. The curved handle at the bottom of the unit holds the inflation bulb ergonomically, and a detachable back cover provides access to the four-slot AA battery holder. All CAD files, PCB schematics, gerber files, and Arduino source code are freely available through the Open Science Framework repository.

Validation involved 88 adult volunteers ranging from 18 to 66 years old, including 30 hypertensive and 58 normotensive participants, whose readings were compared against two clinically established devices, the Riester and the Microlife. Against the Riester, the prototype showed a mean systolic bias of just minus 2.17 millimeters of mercury with a standard deviation of 5.36, comfortably within the ANSI/AAMI SP10 criterion of a mean difference within plus or minus 5 millimeters of mercury and a standard deviation no greater than 8. Heart rate agreement was similarly strong. Diastolic measurements showed greater variability, and comparisons against the Microlife device revealed wider limits of agreement, suggesting the diastolic detection threshold could benefit from further refinement. The authors are careful to note that the study does not constitute formal ANSI/AAMI or ISO validation, but the results are comparable to errors reported in the literature for commercial home monitors.

Beyond its clinical promise, the team positions PressSure as an educational and research platform. Because every design file is open, students can study pressure sensing, analog signal conditioning, oscillometric estimation, and embedded firmware on real hardware, while researchers can swap in alternative sensors, displays, or algorithms to test new ideas. The bill of materials reads like a lesson in frugal engineering: a $1.33 cuff, a $0.47 latex inflation bulb, a $1.17 microcontroller, a $1.55 TFT display, and $2 worth of ABS filament, among other parts. With mass production and a customized display, the authors estimate the unit cost could fall below $5.

The broader implications extend past the device itself. Studies have found that the average price of blood pressure monitors among the best-selling listings in ten countries is around $32, and the World Health Organization has emphasized that an ideal device for low-resource settings must be accurate, durable, affordable, and usable with minimal training. PressSure ticks every box while sidestepping the calibration drift and measurement inconsistency that plague many inexpensive digital monitors. Home measurements also help avoid white-coat hypertension, the well-documented phenomenon in which readings taken in clinical settings are artificially elevated by patient anxiety. By putting a validated, repairable, and transparent measurement tool into the hands of communities that need it most, this $8 device demonstrates that the barriers to global cardiovascular care may be more engineering than economics.

Subject of Research: Design and validation of a low-cost semi-automatic oscillometric blood pressure monitoring device for resource-constrained settings

Article Title: A comprehensive approach to the design and development of a low-cost semi-automatic blood pressure monitoring device for resource-constrained

Article References: Al-Ayyad, M., Moh’d, B. A.-H., Shamsan, M. H., Ahmad, M. A.-S., Al-Takrouri, M., & Owida, H. A. (2026). A comprehensive approach to the design and development of a low-cost semi-automatic blood pressure monitoring device for resource-constrained. HardwareX, Article e00843. https://doi.org/10.1016/j.ohx.2026.e00843

Image Credits: AI Generated

DOI: Not provided

Keywords: blood pressure monitor, hypertension, open-source hardware, oscillometric measurement, Arduino, ATmega328P, 3D printing, low-cost medical devices, signal processing, artifact rejection, resource-limited settings, biomedical engineering

Droplet-based microfluidics has quietly become one of the foundational technologies of modern lab-on-a-chip science. By dispersing femtoliter-to-microliter volumes of fluid as discrete droplets inside a carrier oil, researchers can turn a single sample into thousands of isolated microreactors. That capability underpins single-cell analysis, digital polymerase chain reaction, nanoparticle synthesis, drug screening, enzyme kinetics studies, and even food quality testing. But every one of these applications shares a critical bottleneck: someone has to measure the droplets. Flow rate, droplet velocity, droplet length, and monodispersity — the degree to which droplets in a population are the same size — directly govern mixing efficiency, reaction reproducibility, and the stability of the entire flow regime.

Until now, the gold standard for characterizing those droplets has been high-speed imaging coupled with off-chip image processing, numerical modeling, and increasingly machine learning pipelines. These approaches deliver excellent resolution, but they demand expensive optical infrastructure, dedicated computational post-processing, and often a trained specialist to keep everything aligned and running. Electrode-based alternatives exist, embedding sensing electrodes directly into the channel wall, but they are sensitive to the ionic composition of the fluid and typically require specialized microfabrication, limiting how easily designs can be shared or adapted between laboratories. Neither approach is well suited for the point-of-care and field-deployable settings where droplet microfluidics arguably matters most.

The new platform, developed by Daniel Solano, José Isabel Gómez Quiñones, and Sergio Camacho-León, sidesteps these constraints with a two-stage optoelectrical architecture built from off-the-shelf components. A green LED shines light across a flow-focusing microchannel molded into a translucent PMMA chip the size of a standard microscope slide. As each water-in-oil droplet crosses the optical path, it briefly blocks the transmitted light, producing a characteristic dip in the measured illuminance. The sensor exploits that optical signature twice: once for velocity, once for characterization.

The first sensing stage is a velocity board carrying two surface-mount light-dependent resistors separated by a precisely fixed distance of 9.88 millimeters. When a droplet passes the first LDR, a Schmitt-trigger comparator circuit built from an LM393 and two digitally controlled potentiometers converts the analog signal into a clean digital pulse, and an interrupt routine on the Arduino Zero timestamps the event to the microsecond. The same happens at the second LDR. Dividing the known sensor separation by the transit time yields droplet velocity directly on the chip — a classic time-of-flight measurement, implemented for a few dollars in parts. Interrupt-driven acquisition means the timing accuracy does not depend on the main program loop, which matters when transit times shrink to tens of milliseconds at the top of the measurable range.

The second stage performs full droplet characterization with a TSL2561 digital ambient light sensor sitting beneath the channel. Firmware samples the lux signal roughly 58 times per second — limited by the sensor’s 13-millisecond integration time — and logs the raw data to a microSD card. Then comes the key innovation of this iteration: in an earlier proof-of-concept, the droplet-length pipeline ran off-chip in MATLAB on a host computer. Here, the entire processing chain — baseline subtraction, zero-crossing edge detection, edge refinement, pairing of droplet entry and exit events, and statistically robust aggregation using Welford’s algorithm — executes on the microcontroller itself. The device prints out droplet count, mean length, standard deviation, volume, and coefficient of variation without a computer in the loop, and it logs everything to the SD card for later review.

The validation results are respectable for hardware this inexpensive. On a 1.4-millimeter-wide flow-focusing chip, the sensor characterized 119 droplets and reported a mean length of 4.006 millimeters with an average relative error of 2.06 percent against reference measurements processed with the Fiji image-analysis package. On a narrower 700-micrometer chip, it measured 35 droplets with a mean length of 1617 micrometers, a 2.60 percent mean error, and a notably tight monodispersity of 2.35 percent — and it did so without recalibration, simply by swapping the replaceable microfluidic module. Across six flow-rate ratios tested in the velocity study, covering 797 droplets, on-chip velocity readings ranged from 3.7 to 7.4 millimeters per second with an average deviation of 5.32 percent from a high-speed camera reference. The velocity data also revealed a clean inverse power-law relationship between droplet velocity and monodispersity, with coefficient of variation dropping from roughly 10 percent to about 2 percent as velocity fell toward the model threshold — a practical rule of thumb for operators who need maximally uniform droplets.

The limitations are honestly documented rather than hidden. The LDR response time, with a measured fall time of about 36.75 milliseconds, caps resolvable velocities — up to 522 millimeters per second for long droplets but only around 87 millimeters per second for the shortest detectable 1-millimeter droplets. The team notes that swapping LDRs for photodiodes with transimpedance amplifiers would extend that range at modest added cost and complexity. The two measurement stages currently operate sequentially rather than concurrently, a firmware constraint rather than a physical one, and the platform assumes droplet velocity stays close to its mean, which holds for well-behaved flow-focusing generators. The empirical velocity–monodispersity relationships are explicitly tied to the water-in-oil system and geometry tested and would need fresh validation elsewhere.

What makes the work significant is less any single number than the combination it achieves. No previous low-cost open-source device reports droplet length, volume, velocity, and monodispersity from fully on-device processing while remaining compatible with standard flow-focusing generation — a gap the authors verified against a feature-by-feature comparison of the existing literature. The design deliberately follows the WHO ASSURED criteria for accessible point-of-care diagnostics: affordable, sensitive, specific, user-friendly, rapid, robust, equipment-free, and deliverable. The three custom PCBs use hand-solderable components and can be chemically etched in-house or sent to any commercial fab. The enclosure prints in about half a dollar of black PLA, which doubles as optical shielding against ambient light. The PMMA chip is CNC-milled and solvent-bonded for roughly a dollar.

For a field racing toward portable diagnostics and distributed manufacturing, a $104 instrument that replaces a high-speed camera, a computer, and a proprietary software stack is more than a convenience — it is a removal of a structural barrier. Every design file, from Gerber files and 3D models to Arduino firmware in C++, is public on GitHub and archived on Zenodo, licensed for reuse and modification. A research group in a resource-limited setting can now characterize droplet formation in real time, tune their flow-rate ratios on the fly, and log quantitative data with equipment that fits in a shoebox. If droplet microfluidics is to fulfill its point-of-care promise, instruments like this one — open, modular, and cheap enough to fail with — are how it gets there.

Subject of Research: An open-source, low-cost optoelectrical sensor for on-chip characterization of droplet velocity, length, volume, and monodispersity in droplet-based microfluidics.

Article Title: An open-source, modular optoelectrical sensor for on-chip flow characterization in droplet-based microfluidics

Article References: Solano, D., Quiñones, J. I. G., & Camacho-Leon, S. (2026). An open-source, modular optoelectrical sensor for on-chip flow characterization in droplet-based microfluidics. HardwareX, Article e00835. https://doi.org/10.1016/j.ohx.2026.e00835

Image Credits: AI Generated

DOI: 10.1016/j.ohx.2026.e00835

Keywords: microfluidics, droplet-based microfluidics, open-source hardware, optoelectrical sensor, lab-on-a-chip, point-of-care diagnostics, Arduino, flow-focusing, monodispersity, PMMA microfluidic chip, Lab-on-PCB, ASSURED criteria