Sensor and range-of-motion lab
Open your materials, follow the steps, then turn in your work.
Collect EMG or range-of-motion data and record results in a data table.
1. Open your materials
Use the materials named in the first step below. Open lesson resources.
2. Start the work
Set up the EMG sensor or goniometer and zero the baseline.
Show all 5 required steps
- Set up the EMG sensor or goniometer and zero the baseline.
- Run repeated trials of a grip or joint movement until fatigue appears.
- Record force or angle and time for each trial in your data table.
- Note the trial where performance clearly dropped.
- Submit your raw data table with units and the fatigue-onset trial marked.
Lost your place? Lost your place? You are running the sensor lab. Zero your EMG sensor or goniometer, run repeated trials until fatigue shows, record value plus units plus any observation each trial, mark the fatigue-onset trial, then submit the raw table.
Check your work before submitting
- You can collect clean motion or EMG data with units.
- You can identify the trial where fatigue begins.
Before lab work: read the safety rules
- Confirm no skin allergies to electrode gel or adhesive before applying EMG electrodes.
- Do not apply electrodes over broken skin, rashes, or open wounds.
- Stop the trial immediately if a participant reports sharp pain rather than muscle fatigue.
- Dispose of single-use electrode pads in regular trash; do not reuse.
3. Turn in your work
DueCheck Schoology- Hand in
- Raw data table with trial number, measured value (mV or degrees, with units), time, and fatigue-onset trial clearly marked.
How to submit and name your file
Submit your completed raw data table.
In Schoology, open your course and the assignment for this lesson. Attach your file, select Submit, and check that it appears in the submission.
PDF upload helpYou get two school days for every day you were absent, so this deadline moves with you.
Choose your Schoology section. Open only one assignment.
Check the section number beside Human Anatomy and Physiology in Schoology.
Assignment: Wk7 Data table: Sensor and range-of-motion lab
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How this lesson connects
Keep using what you learned last class: Wearables read your body's data outside a clinic, so the rules that would protect that data at a doctor's office often do not apply, leaving privacy and workplace safety in direct tension. Today: A conclusion is only as good as the data under it, so you zero your sensor and run consistent trials in order to tell a real fatigue trend apart from a single noisy reading.
Unit 1 guide: what to keep and use nextOptional: listen or watch a unit review▸
Need help? Warm-up, timing, and directions▸
💡 Big idea: A conclusion is only as good as the data under it, so you zero your sensor and run consistent trials in order to tell a real fatigue trend apart from a single noisy reading.
- 0-10 and sensor setup; zero baseline and practice trial
- 10-20Protocol review: number of trials, rest intervals, recording format
- 20-50Run trials; record force or angle and time in
- 50-62Mark fatigue-onset trial; add qualitative observations column
- 62-75Peer-check: does each row have units? Is fatigue-onset trial marked?
- 75-80Submit raw ; clean up sensors
- • Today is a sensor lab. You are collecting real data from real muscles.
- • The protocol is simple: set up, zero the sensor, run trials, record everything. But the discipline of recording every trial with units is what makes this science.
- • Your today is raw evidence. Do not average or analyze yet. Thursday is for analysis.
- • Mark the trial where you first see a consistent drop. That is your fatigue-onset marker.
- • A goniometer measures angle in degrees; EMG sensors measure muscle electrical activity in millivolts. Both require zeroing before data collection.
- • A must include: trial number, measured value (with units), and any qualitative observation (pain, tremor, noticeable fatigue).
- • Identifying the fatigue-onset trial requires looking for a consistent downward trend in force or angle, not a single low value.
PLTW connection and today's work
Complete the data-collection prompts in Activity 1.2.4 Mind Over Muscle (the sensor half) and Activity 1.2.5 Joints in Motion (the range-of-motion half) on myPLTW that correspond to today's lab stations.
Today's stopping point: EMG basics task is done; today the lab task should show complete alongside your data table.
PLTW activity titles identify the course connection. If your account will not open, use the posted materials for today and tell Mr. Mendoza. Do not mark an online activity complete unless you completed it.
Course connection
- Activity 1.2.4 Mind Over Muscle
- Activity 1.2.5 Joints in Motion
Use the turn-in directions at the top of this page. Do not create a second submission unless your teacher asks for one.
Show another explanation or a smaller first step
Need help? Choose a starting point
Finish the assigned lab safely before starting extra practice.
Lesson resources: reading, slides, 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.
Wearables read your body's data outside a clinic, so the rules that would protect that data at a doctor's office often do not apply, leaving privacy and workplace in direct tension.
A conclusion is only as good as the data under it, so you zero your sensor and run consistent trials in order to tell a real fatigue trend apart from a single noisy reading.
A mechanic studies a tool whose shape allows one job but limits another.
- Which feature makes the tool work?
- What changes if that feature bends or breaks?
- Which observation shows function rather than appearance?
Structure creates possibilities and limits for function.
Living tissues adapt and interact with other systems; a metal tool does not.
- • Tool shape maps to .
- • The job maps to physiological function.
- • Damage maps to a predicted functional change.
Driving question: How do you tell the exact trial where your muscle actually started to fatigue apart from a trial that was just a little low by chance?
What you already know: Wearables read your body's data outside a clinic, so the rules that would protect that data at a doctor's office often do not apply, leaving privacy and workplace in direct tension.
New idea: A conclusion is only as good as the data under it, so you zero your sensor and run consistent trials in order to tell a real fatigue trend apart from a single noisy reading.
Visual or model: F1. F1. A lesson illustration or teaching diagram for Sensor and range-of-motion lab. Use it with E1-E3; it is a model or context image, not experimental or patient data. What to notice: Trace the labeled structure, movement, or system relationship that connects form to function.
- Observe or measure the relevant feature in today's lesson.
- Organize the observation with a stable evidence ID.
- Apply this rule: Structure creates possibilities and limits for function.
- Choose the option the evidence supports and state the limit of the conclusion.
Real biomedical example: How do you tell the exact trial where your muscle actually started to fatigue apart from a trial that was just a little low by chance?
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 full distance and direction a can move, measured in degrees from its fully bent to its fully straightened position.
- • : Bending a so the angle between two body parts decreases, such as curling the forearm toward the shoulder.
- • extension: A movement that increases the angle of a and straightens a body part, such as opening the arm to straighten the elbow.
- • fatigue: A state of physical or mental tiredness in which muscles or the body produce less force or focus and need rest to recover.
- • goniometer: Use the lesson context and glossary entry to explain goniometer in your own words.
- • biomechanics: The study of how forces, motion, and structure act on living bodies, applying physics to muscles, bones, and joints.
- • : The scientific study of human movement, including how muscles, bones, and the nervous system work together to produce motion.
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.
Musculoskeletal function depends on the interaction of structure, force, joints, neural activation, and loading history, so one model or measurement captures only part of performance.
Limit: A classroom model or movement measure cannot diagnose an injury or prescribe .
Structure creates possibilities and limits for function.
Limit: Living tissues adapt and interact with other systems; a metal tool does not.
You can collect clean motion or EMG data with units.
Limit: E3 defines the classroom product or success criterion. It is not independent scientific evidence and cannot justify a clinical or causal claim.
PLTW-HAP-2027-03-01 · Simulated classroom evidence scenario
Your role: anatomy and physiology consultant
Decision: Your team must decide what the evidence from today's lesson supports before submitting the labeled and result claim named on today's page.
- • Find out whether my effort dropped on that trial, because the sensor cannot tell tiredness from distraction.
- • Zero the sensor and run consistent trials, then look for a downward trend across several readings.
- • Mark the trial with the lowest reading as the exact moment my muscle started to fatigue.
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: Today's evidence supports a classroom claim about today's lesson. It cannot prove causation, diagnose a real patient, or justify action outside this room.
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.
Students often think Students think the single lowest reading is the moment fatigue hit.. The trap: One low value can be a slip, a distraction, or noise, because real fatigue shows up as a consistent downward trend across trials, not a lone dip that the next trial recovers from.
Setup note: zeroed the goniometer baseline at 0 degrees before the first trial.
Raw data (grip-angle trials, fictional Sample 1):
- Trial 1: 142 degrees, 0 s, steady, no fatigue.
- Trial 2: 140 degrees, 10 s, steady.
- Trial 3: 138 degrees, 20 s, slight tremor noted.
- Trial 4: 129 degrees, 30 s, noticeable drop, mild ache. <-- fatigue onset
- Trial 5: 121 degrees, 40 s, clear decline.
- Trial 6: 118 degrees, 50 s, plateau low.
Fatigue-onset trial: Trial 4, because that is where a consistent downward trend begins (not just one low value), and the participant first reported effort and tremor.
| Trial | Angle (degrees) | Time (s) | Observation |
|---|---|---|---|
| 1 | 142 | 0 | Steady |
| 2 | 140 | 10 | Steady |
| 3 | 138 | 20 | Slight tremor |
| 4 | 129 | 30 | Drop, fatigue onset |
| 5 | 121 | 40 | Clear decline |
This model shows the level of evidence and organization needed to complete: A raw data table recording each trial of a fatiguing movement with the measured value and units, the time, an observation, and the fatigue-onset trial clearly marked.
- 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 completed raw data table.
- 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 Sensor and range-of-motion 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.
Hand-picked readings, videos, and interactives for this lesson, all free and from authoritative open organizations (NIH, CDC, OpenStax, Khan Academy, PhET, HHMI, and more).
A fillable, Cornell-style notebook for Unit 1: Road to Rehabilitation. Type your notes, cues, and summaries right in the PDF, or print it and write by hand. Each lesson page has a cue column, a notes column, and a summary box, plus dated lab-record pages you can turn in.
HBS Unit 1 notebook: Road to Rehabilitation Fillable PDFCornell notes + lab recordsOpenVetted readings and references for this unit. Use them to prepare, to catch up if you were absent, or to go deeper on today's target.
Practice: try a question, then check your answer▸
Claim ceiling for this check: Today's evidence supports a classroom claim about today's lesson. It cannot prove causation, diagnose a real patient, or justify action outside this room.
Your grip force readings are 42, 41, 43, 38, 30, 25 (in order). Which trial marks fatigue onset, and why not the trial with the 38?
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.
Missed class or ready for more?▸
Run this before you touch the bench. It is built from the real lab procedure, so the decisions you make here are the ones you will make with the equipment in your hands.
I can name the procedure's purpose and the evidence I will record. I can name today's hazards and the control for each: Stop the trial immediately if a participant reports sharp pain rather than muscle fatigue. My data table is ready before materials are handled.
Finish the checklist before you handle any material.
- • Confirm no skin allergies to electrode gel or adhesive before applying EMG electrodes.
- • Do not apply electrodes over broken skin, rashes, or open wounds.
- • Stop the trial immediately if a participant reports sharp pain rather than muscle fatigue.
- • Dispose of single-use electrode pads in regular trash; do not reuse.
- 1Before materials are handled, identify the purpose, variables or comparison, controls, measurement units, and stop-work condition.
- 2Set up the EMG sensor or goniometer and zero the baseline.
- 3Run repeated trials of a grip or joint movement until fatigue appears.
- 4Record force or angle and time for each trial in your data table.
- 5Note the trial where performance clearly dropped.
- 6Submit your raw data table with units and the fatigue-onset trial marked.
- 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 the linked simulation or the teacher's posted EMG dataset to record trials of a fatiguing movement, build a with units, and mark where performance dropped, then submit it.
PhET SimulationsSubmit your completed raw data table.
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: Joints and Movement- 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.
- SubmittedGo to Schoology to turn this in. Submit one PDF. Put your first and last name in the document header. Name the file: FirstName LastName - Assignment Title - YYYY-MM-DD.pdf. If you cannot get in, see Mr. Mendoza. Do not skip the work.
- 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.
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