Physiology sensor lab
Safety gate · before any work
- Wash hands before placing any sensor on a participant.
- Do not share sensor components that contact skin without wiping with an alcohol swab between uses.
- Stop the trial immediately if a participant reports discomfort, dizziness, or pain.
Do now
Collect physiological data using sensors under controlled conditions.
- Hand in
- Raw physiology data table: labeled trials, baseline and treatment conditions, measurement values with units, and a brief condition-control note.
- Where
- Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not.
You get two school days for every day you were absent, so this deadline moves with you.
Can you run your physiology sensor so cleanly that another group, handed only your protocol and , would get the same readings?
Collect physiological data using sensors under controlled conditions.
- • Your records repeated, labeled trials.
- • You can describe how you controlled conditions.
- Why might reading a sensor once give you a number you should not trust?
- Name one condition you would keep the same across every trial so your readings can be compared.
- 1Set up the sensor and calibrate it per the protocol.
- 2Record baseline and treatment measurements for each trial.
- 3Repeat trials to capture variation.
- 4Log all readings in a structured .
- 5Submit your raw .
What did this day actually feel like?
Physiology sensor lab
LAB Sensors on people, multiple trials, units on everything. Our first trials were inconsistent because we were not standardizing rest between them.
The protocol has to control the subject as carefully as the instrument.
Turned in: data table → Data Tables folder
Fiction. There is no such student. The lessons, labs and dates are the real planned course; the student, the classmates and the conversations are invented.
The same day, drawn.
Sensors on people, multiple trials, units on everything. Our first trials were inconsistent because we were not standardizing rest between them.
Fiction. There is no such student. The lessons, labs and dates are the real planned course; the student, the classmates and the conversations are invented.
🛠 Get unstuck · pick your level
Lab day: Tier 1 is the whole class at the bench. No extension today.
🔑 Today's words · 5
Tap a word in the lesson for a plain meaning and one example. Recycled into next week's Do-Now.
Do the work · 80-minute blockfirst 5 min = hook▸
💡 Big idea: Single readings hide normal variation, so repeated, calibrated, real-time trials are required for a to count as reproducible scientific evidence.
- 0-10 and calibration check: set up sensor, verify calibration, and confirm is open
- 10-20Baseline trials: record baseline measurements for each participant or condition
- 20-50Treatment trials: apply the and record all readings per trial
- 50-65Repeat trials as needed to capture natural variation; log all readings
- 65-75Submit raw before leaving the lab area
- 75-80Exit note: describe how you controlled conditions and whether anything went wrong
- • Today is the hands-on data collection session for Problem 2.
- • Bring your pre-lab and your hypothesis -- you should already know what you expect to see before you touch the sensor.
- • Every trial you run goes directly into the table in real time. No copying from memory after the fact.
- • Lab SOPs and Microbiology Testing on WebXam 072125 both test your ability to follow protocols and record data correctly.
- • How to calibrate and operate a physiological sensor following a written protocol (Lab SOP).
- • Why repeating trials and recording each reading separately is essential for reliable conclusions.
- • How a structured with labeled conditions and units enables statistical analysis.
Experimental vs observational studies, sample size, graphing, mean, SD, t-test purpose. · Physiology sensor lab
Day 3 of this lesson. Open this exact section in myPLTW (find it in Clever, Microsoft sign-in), then do the work below.
Do this: Open Problem 2 in your myPLTW course shell and locate the physiology data-collection protocol to review the sensor setup steps and data-table format.
Mark the data-collection lab activity complete in your tracker after submitting your raw .
The study design and sample-size plan are done; today you run the sensor lab and submit your completed raw .
Completed raw with trial numbers, conditions, measurement values, and units turned in on the class site or in person.
The official PLTW activity stays inside myPLTW. If myPLTW will not open, use F1 and E1-E3 on this page to complete today's local evidence decision, then make up the official activity when access returns. Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not.
Check things off as you work, then submit. This tells Mr. Mendoza how you're doing so he can help the class. It does not replace turning in your producible through the submission route shown below.
Use the code Mr. Mendoza gave you, not your name. Saved on this device.
Experimental vs observational studies, sample size, graphing, mean, SD, t-test purpose. · Physiology sensor lab
Open Problem 2 in your myPLTW course shell and locate the physiology data-collection protocol to review the sensor setup steps and data-table format.
The study design and sample-size plan are done; today you run the sensor lab and submit your completed raw .
This is how Mr. Mendoza sees the class keeping pace with PLTW. Be honest, it only helps if it is accurate.
🎯 Collect physiological data using sensors under controlled conditions.
- Set up the sensor and calibrate it per the protocol.
- Record baseline and treatment measurements for each trial.
- Repeat trials to capture variation.
- Log all readings in a structured .
- Submit your raw .
Data table: Raw physiology : labeled trials, baseline and treatment conditions, measurement values with units, and a brief condition-control note.
Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not. Use the checklist just below and upload by 11:29 PM for full credit. Absent with an excused absence? You get two school days for every day you were absent, so this deadline moves with you.
| Task | Who |
|---|---|
| Set up the sensor and calibrate it per the protocol. | _______ |
| Record baseline and treatment measurements for each trial. | _______ |
| Repeat trials to capture variation. | _______ |
| Log all readings in a structured . | _______ |
| Submit your raw . | _______ |
Working solo? Put your own name in "Who" for every row.
- Your records repeated, labeled trials.
- You can describe how you controlled conditions.
- 1Do thisCollect physiological data using sensors under controlled conditions.
- 2Use this resource
- 3Submit thisData table: Raw physiology data table: labeled trials, baseline and treatment conditions, measurement values with units, and a brief condition-control note.
- 4Submit it here
- 1Open the drop folder.
- 2Sign in with your district Microsoft account, not a personal one.
- 3Upload the file, named Lastname_Firstname__Assignment Title.
- 4Your own upload panel says Uploaded with a green check: that is your receipt.
Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not. Biotechnology for Health (Biomedical Innovations) › Experimental vs observational studies, sample size, graphing, mean, SD, t-test purpose. › Data tableOpen the drop folder
Learn it · deck, reading, and vocabulary▸
The deck carries the prior idea forward, lets you inspect an analogy, maps the rule to biology, and ends with the same evidence decision and exit ticket used on this page.
Generated from this lesson's canonical data with a red-team citation check.
Human physiological data can expose and harm a real person, so consent, data minimization, and storage limits exist to protect that person rather than to satisfy bureaucracy.
Single readings hide normal variation, so repeated, calibrated, real-time trials are required for a to count as reproducible scientific evidence.
A detective board holds observations, possible explanations, and one next question.
- Which notes are direct observations?
- Which notes are explanations?
- What new evidence would separate the explanations?
Keep observations separate from explanations, then collect the evidence that can distinguish the options.
Biomedical investigations use controlled procedures and validated measurements, not intuition alone.
- • Board notes map to E1-E3.
- • Possible explanations map to the decision options.
- • The next question maps to the evidence-based action.
Driving question: Can you run your physiology sensor so cleanly that another group, handed only your protocol and , would get the same readings?
What you already know: Human physiological data can expose and harm a real person, so consent, data minimization, and storage limits exist to protect that person rather than to satisfy bureaucracy.
New idea: Single readings hide normal variation, so repeated, calibrated, real-time trials are required for a to count as reproducible scientific evidence.
Visual or model: F1. F1. A lesson illustration or teaching diagram for Physiology sensor lab. Use it with E1-E3; it is a model or context image, not experimental or patient data. What to notice: Trace the labeled system, test, or design relationship and identify which evidence should trigger revision.
- Observe or measure the relevant feature in Physiology sensor lab.
- Organize the observation with a stable evidence ID.
- Apply this rule: Keep observations separate from explanations, then collect the evidence that can distinguish the options.
- Choose the option the evidence supports and state the limit of the conclusion.
Real biomedical example: Can you run your physiology sensor so cleanly that another group, handed only your protocol and , would get the same readings?
What the evidence supports: E1-E3 and F1 support the daily take-home when the response meets the stated success criteria.
What it cannot prove: The package does not support claims beyond this lesson's or any real patient diagnosis.
- • : The number of subjects or observations in a study; larger samples give more reliable results and reduce the role of chance.
- • mean: The average of a set of numbers, found by adding all the values together and dividing by how many values there are.
- • : A number that measures how spread out data values are around the mean; a small value means values cluster tightly, a large value means they scatter.
- • t-test: A statistical test that compares the average values of two groups to judge whether their difference is likely real or just due to chance.
- • validity: How well a test or study actually measures what it claims to, so the conclusions truly reflect reality.
- • reliability: The degree to which a measurement, method, or person produces the same dependable result each time under the same conditions.
Use it now: Choose one decision option. Cite E1 and E3, then explain how the rule connects the evidence to your choice.
Go further, optional: The source links below are optional enrichment. Every fact required for today's local evidence decision appears in this lesson package.
How to calibrate and operate a physiological sensor following a written protocol (Lab SOP).
Limit: E1 supplies context or an observation; it does not by itself establish the explanation.
Single readings hide normal variation, so repeated, calibrated, real-time trials are required for a to count as reproducible scientific evidence.
Limit: E2 is a teaching statement or comparison and must be checked against the task evidence.
Your records repeated, labeled trials.
Limit: E3 supports only the result or product criterion named here; it cannot justify a broader clinical or causal claim.
PLTW-BFH-2027-03-04 · Simulated classroom evidence scenario
Your role: biomedical design team member
Decision: Your team must decide what the evidence from Physiology sensor lab supports before submitting the labeled and result claim named on the lesson page.
- • Proceed because the readiness evidence is complete.
- • Pause and correct the named setup or gap.
- • Repeat the measurement because quality controls are not acceptable.
Response: State one choice, cite at least two evidence IDs, explain the rule that connects them, and add one limitation. Submit it as the labeled and result claim.
Claim ceiling: The supplied lesson evidence can support an observation, pattern, classroom mechanism, or next-step decision about Physiology sensor lab. It cannot by itself prove causation, establish a real clinical diagnosis, or justify action outside this classroom task.
Reason for review: Your team must decide what the evidence from Physiology sensor lab supports before submitting the labeled and result claim named on the lesson page.
Context: Evidence comes from repeated, controlled, real-time measurement, so a that no one could reproduce is not scientific evidence no matter how neat it looks.
- • T1: Set up the sensor and calibrate it per the protocol.
- • T2: Record baseline and treatment measurements for each trial.
- • T3: Repeat trials to capture variation.
- • T4: Log all readings in a structured .
- • T5: Submit your raw .
- • E1: How to calibrate and operate a physiological sensor following a written protocol (Lab SOP).
- • E2: Single readings hide normal variation, so repeated, calibrated, real-time trials are required for a to count as reproducible scientific evidence.
- • E3: Your records repeated, labeled trials.
Measurements: Use only the measurements, units, graph, or counts supplied in today's task. No additional patient measurement is implied.
Figure finding: Teaching diagram for Physiology sensor lab. Trace the labeled system, test, or design relationship and identify which evidence should trigger revision. This is a teaching model, not patient or experimental data.
Uncertainty: This is a composite classroom scenario. Missing history, measurements, or confirmation tests remain unknown and limit the conclusion.
Mean = sum of values / number of values. Median = middle ordered value. Range = maximum - minimum.
For 2, 4, 4, and 10: mean = 20 / 4 = 5, median = 4, and range = 10 - 2 = 8.
Mean, median, and range keep the measurement unit. Order the values before finding the median.
Calculate the requested summary for today's supplied values, then write what it reveals and what it hides.
- • The solution must address the stated need in Physiology sensor lab.
- • The decision must be supported by E1-E3.
- • The final product must make the success criteria visible.
- • Complete the work inside the 80-minute block.
- • Use only supplied or teacher-approved materials and evidence.
- • Do not trade , accessibility, or privacy for speed.
- • and evidence quality: must pass before scoring other criteria.
- • User need and effectiveness: highest scored criterion.
- • Time, cost, and ease of use: compare only after and effectiveness pass.
Test evidence: For each option, record the E1-E3 result that supports or fails each criterion. Do not assign a score without a named observation.
- Version or option tested
- Criterion met or missed
- Evidence ID and result
- Revision made
- Reason for the revision
- Need and user
- Criteria and constraints
- Chosen option and evidence
- Test result
- Revision and reason
Students often think Students believe one careful measurement is enough if they were focused and the device looked accurate.. The trap: A single reading cannot show reliability, because you have no way to see the normal trial-to-trial variation, so repeating trials and recording each one separately is what turns a number into evidence.
Research question: Does light activity raise resting heart rate?
Sensor: pulse/heart-rate sensor, calibrated against a 60-second manual pulse count before trial 1.
Conditions controlled: same subject (Subject A), seated for baseline, same room temperature, 2-minute rest between trials.
I recorded five repeated trials for each condition and logged every reading separately rather than averaging in my head:
- Baseline (resting), trials 1-5: 72, 70, 74, 71, 73 bpm
- Treatment (after 1 min stepping), trials 1-5: 96, 99, 94, 101, 98 bpm
Condition-control note: I kept the subject, sensor placement, and rest interval the same across all trials so that the only thing changing was rest vs light activity. That is what lets me trust a difference if I find one.
| Trial | Condition | Heart rate (bpm) |
|---|---|---|
| 1 | Baseline rest | 72 |
| 2 | Baseline rest | 70 |
| 3 | After stepping | 96 |
| 4 | After stepping | 99 |
| 5 | After stepping | 94 |
This model shows the level of evidence and organization needed to complete: Completes the Problem 2 sensor-lab data-collection step: a structured raw data table of repeated, controlled physiology trials ready for analysis.
- Name the variables and include units.
- Enter observations without changing the raw values.
- Check labels, calculations, and patterns before interpreting the data.
Keep the structure. Replace the question, facts, measurements, and evidence. Then recheck units, vocabulary, and whether the conclusion goes beyond the evidence.
Also due today: Submit your raw data table on the class site, or hand it to Mr. Mendoza in class before leaving the lab area.
- CER:
- Claim, Evidence, Reasoning: make a claim, back it with evidence, explain your reasoning.
- SOP:
- Standard Operating Procedure, the exact steps to follow (especially in a lab).
- Tracker:
- Your PLTW progress log where you record completed evidence.
- myPLTW:
- The PLTW course site where you do the online activities. Find it in Clever with your Microsoft sign-in, right next to Schoology.
Tap the speaker to hear a term. Add two of these to your notebook glossary with a definition and an example in your own words.
Pick just 2 or 3 words from today and make them yours: write what each one means in your own words, name the context clue or evidence that helped, then give one example from what you actually did in Physiology sensor lab. Try your own words first; the glossary is there if you get stuck. This is voluntary and counts as extra credit, so keep it short.
Saved on this device. Show Mr. Mendoza or add these to your notebook glossary to claim the extra credit.
Classroom documents for this lesson are posted in Schoology. Open Clever, then Schoology, and find each one by the name shown on its card.
Use this if you were absent, got stuck, or need another pass before you submit the lesson artifact.
Placement rationale
Matched Human physiology data and research design by path:Biomedical-Innovations/Problem-2_Human-Physiology/2.1_Human-Physiology; keywords:physiology, research design. Score 142. Visibility: student-schoology (student-facing resource; link through Schoology rather than local path).
Use this if you were absent, got stuck, or need another pass before you submit the lesson artifact.
Placement rationale
Matched Human physiology data and research design by path:Biomedical-Innovations/Problem-2_Human-Physiology/2.1_Human-Physiology; keywords:physiology, research design. Score 142. Visibility: student-schoology (student-facing resource; link through Schoology rather than local path).
Use this if you were absent, got stuck, or need another pass before you submit the lesson artifact.
Placement rationale
Matched Human physiology data and research design by path:Biomedical-Innovations/Problem-2_Human-Physiology/00_Problem-Overview; keywords:physiology, research design. Score 138. Visibility: student-schoology (student-facing resource; link through Schoology rather than local path).
How to get there: open Clever and sign in with your Microsoft (district) account. Both myPLTW and Schoology are in Clever. Do the activity in myPLTW. Turn the work in on this site or hand it to Mr. Mendoza, because that is the step that counts as submitted. Schoology only shows your report-card grade later.
Check yourself · commit, then reveal▸
Claim ceiling for this check: The supplied lesson evidence can support an observation, pattern, classroom mechanism, or next-step decision about Physiology sensor lab. It cannot by itself prove causation, establish a real clinical diagnosis, or justify action outside this classroom task.
Your table shows one baseline heart rate and one exercise heart rate. Your partner says 'that proves exercise raised it.' Why is one trial each not enough, and what fixes it?
Write an answer and pick a confidence to unlock the key.
Fast retrieval with instant answers, not the commit-then-reveal check above. Try each from memory first: write what you remember about the earlier units, then check yourself here.
Go further and get help▸
I can name the procedure's purpose and the evidence I will record. I can identify each named hazard and the control that reduces it: Wash hands before placing any sensor on a participant. My data table is ready before materials are handled.
Finish the checklist before you handle any material.
- • Wash hands before placing any sensor on a participant.
- • Do not share sensor components that contact skin without wiping with an alcohol swab between uses.
- • Stop the trial immediately if a participant reports discomfort, dizziness, or pain.
- • Do not collect data on a participant who has not verbally consented for this class activity.
- • Follow the instructor's calibration protocol exactly -- do not skip steps.
- • Store sensor equipment in the labeled case when not in use; report any damage immediately.
- 1Before materials are handled, identify the purpose, variables or comparison, controls, measurement units, and stop-work condition.
- 2Set up the sensor and calibrate it per the protocol.
- 3Record baseline and treatment measurements for each trial.
- 4Repeat trials to capture variation.
- 5Log all readings in a structured data table.
- 6Submit your raw data table.
- 7Record each result in the prepared table before interpreting it. Mark missing, repeated, or invalid results truthfully.
- 8Complete the named cleanup and waste route, remove PPE safely, wash hands when required, and confirm the station is ready for the next group.
| Trial or sample ID | Independent condition | Measured result with units | Observation before interpretation | Quality-control note |
|---|---|---|---|---|
Before the procedure, predict the result and cite the rule behind the prediction.
After the procedure, compare the result with the prediction and name one limitation or source of uncertainty.
What today's skills lead to. These are real health-science careers this course builds toward. Tap one to see, on the US Department of Labor's O*NET site, what the job actually involves, what it pays, and how fast it is growing.
Use a teacher-provided physiology dataset or a PhET simulation to generate readings, then build a structured spreadsheet dataset of repeated trials with labeled conditions.
PhET Interactive SimulationsThen submit your Data table. Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not.
Class still runs. Complete the online activity above (it's self-guided). Need the concept taught without a teacher? Use this authoritative explainer:
Khan Academy Statistics and Probability- CompleteEvery required part of the artifact is present, nothing left blank.
- AccurateThe science and the data are correct and match the evidence.
- Scientific reasoningYou explain your claim with evidence and reasoning (CER), not just an answer.
- Professional communicationClear, organized, labeled, and written the way a clinician or scientist would.
- SubmittedTurned in the right way, on the class site or handed to Mr. Mendoza in class, and confirmed. Not in Schoology: that is where the report-card grade appears later.
- Error analysis and method · counts doubleName a specific limit of the method and how it moved your result, and compare what you predicted to what happened. "Human error" does not count; say what about the procedure or instrument caused it.