Sensor and range-of-motion lab
Safety gate · before any work
- 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.
Do now
Collect EMG or range-of-motion data and record results in a data table.
- Hand in
- Raw data table with trial number, measured value (mV or degrees, with units), time, and fatigue-onset trial clearly marked.
- 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.
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?
Collect EMG or range-of-motion data and record results in a .
- • You can collect clean motion or EMG data with units.
- • You can identify the trial where fatigue begins.
- What does it mean to 'zero' a sensor, and why would you do it before collecting any data?
- Name the three things every row of a good needs today.
- 1Set up the EMG sensor or goniometer and zero the baseline.
- 2Run repeated trials of a grip or movement until fatigue appears.
- 3Record force or angle and time for each trial in your .
- 4Note the trial where performance clearly dropped.
- 5Submit your raw with units and the fatigue-onset trial marked.
What did this day actually feel like?
Sensor and range-of-motion lab
LAB Real sensors, real numbers, units on everything. The EMG signal in millivolts, grip force in newtons, joint angle in degrees, multiple trials.
Our first two trials were unusable because the sensor was not seated the same way each time, so we were measuring our own setup instead of the muscle. We restarted, fixed the placement, and wrote the failed trials into the notebook anyway, because deleting them would have been the wrong instinct.
Turned in: raw 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.
Real sensors, real numbers, units on everything. The EMG signal in millivolts, grip force in newtons, joint angle in degrees, multiple trials.
ME
We were measuring our own setup, not the muscle. Start over and seat it the same way each time.
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: 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.
Unit 1.2 Motion Data: Muscle strength, fatigue, physiology sensors, range of motion, joint testing, kinesiology taping. · Sensor and range-of-motion 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: Complete any lab-day check-in or data-collection prompt in Lesson 1.2 Muscles and Motion on myPLTW that corresponds to today's sensor or range-of-motion lab.
Mark the lab task complete after submitting your completed .
EMG basics task is done; today the lab task should show complete alongside your .
myPLTW completion status plus your submitted .
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.
Unit 1.2 Motion Data: Muscle strength, fatigue, physiology sensors, range of motion, joint testing, kinesiology taping. · Sensor and range-of-motion lab
Complete any lab-day check-in or data-collection prompt in Lesson 1.2 Muscles and Motion on myPLTW that corresponds to today's sensor or range-of-motion lab.
EMG basics task is done; today the lab task should show complete alongside your .
This is how Mr. Mendoza sees the class keeping pace with PLTW. Be honest, it only helps if it is accurate.
🎯 Collect EMG or range-of-motion data and record results in a .
- Set up the EMG sensor or goniometer and zero the baseline.
- Run repeated trials of a grip or movement until fatigue appears.
- Record force or angle and time for each trial in your .
- Note the trial where performance clearly dropped.
- Submit your raw with units and the fatigue-onset trial marked.
Data table: Raw with trial number, measured value (mV or degrees, with units), time, and fatigue-onset trial clearly marked.
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 EMG sensor or goniometer and zero the baseline. | _______ |
| Run repeated trials of a grip or movement until fatigue appears. | _______ |
| Record force or angle and time for each trial in your . | _______ |
| Note the trial where performance clearly dropped. | _______ |
| Submit your raw with units and the fatigue-onset trial marked. | _______ |
Working solo? Put your own name in "Who" for every row.
- You can collect clean motion or EMG data with units.
- You can identify the trial where fatigue begins.
- 1Do thisCollect EMG or range-of-motion data and record results in a data table.
- 2Use this resource
- 3Submit thisData table: Raw data table with trial number, measured value (mV or degrees, with units), time, and fatigue-onset trial clearly marked.
- 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. Human Anatomy & Physiology (Human Body Systems) › Unit 1.2 Motion Data: Muscle strength, fatigue, physiology sensors, range of motion, joint testing, kinesiology taping. › 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.
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 Sensor and range-of-motion lab.
- 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.
- • fatigue: A state of physical or mental tiredness in which muscles or the body produce less force or focus and need rest to recover.
- • EMG: Electromyography, a test that records the electrical signals muscles produce when they contract, used to study muscle and nerve function.
- • : 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.
- • 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.
A goniometer measures angle in degrees; EMG sensors measure muscle electrical activity in millivolts. Both require zeroing before data collection.
Limit: E1 supplies context or an observation; it does not by itself establish the explanation.
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.
Limit: E2 is a teaching statement or comparison and must be checked against the task evidence.
You can collect clean motion or EMG data with units.
Limit: E3 supports only the result or product criterion named here; it cannot justify a broader 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 Sensor and range-of-motion 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 Sensor and range-of-motion lab. It cannot by itself prove causation, establish a real clinical diagnosis, or justify action outside this classroom task.
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.
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 Sensor and range-of-motion lab. It cannot by itself prove causation, establish a real clinical diagnosis, or justify action outside this classroom task.
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.
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: Confirm no skin allergies to electrode gel or adhesive before applying EMG electrodes. 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 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: 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.
- 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.