Yeah, this is the wall everyone hits when they start doing IMU-based activity recognition.

You go in thinking, “Walking vs running should be easy,” then you open the data and suddenly everything looks… weirdly similar. And once you start scanning papers, you’re buried under CNN, LSTM, GRU, 1D-CNN, Transformer, HMM, etc. Total model buffet, zero clarity.

Let me save you some time (and sanity).

First: a reality check

IMU activity recognition is hard not because the models suck, but because the problem itself is messy.

• Everyone moves differently

• Sensor placement changes everything

• Walking vs stairs vs slow jogging are basically cousins

• Transitions (stand → walk) are pure chaos

So when you see “just use a Transformer”, that’s usually paper-talk, not production reality.

Before models: the stuff that actually matters

Reddit blunt take:

Models are ~30% of the problem.
Windowing, features, and labels are the other 70%.

If you’re not already doing this, you’re handicapping yourself:

• Sliding windows: ~2–5 seconds
  (shorter = noise, longer = you miss transitions)

• Heavy overlap (50%+)

• Don’t rely only on raw XYZ:

  • magnitude (√x² + y² + z²)

  • jerk (derivative)

  • orientation-independent features

Just fixing this often gives you a bigger gain than switching models.

1)Okay, but which model should you actually pick?

Here’s the honest breakdown.

 “I just need something that works”

* 1D-CNN

This is the boring, reliable workhorse of HAR.

• Fast to train

• Good at waveform patterns

• Works well in real time

• Easier to debug than RNNs

A lot of papers quietly conclude:

“CNN performs as well as or better than LSTM with less complexity.”

Reddit version:

Boring? Yes.
Will it betray you? Rarely.

2) “Temporal structure really matters”

* CNN + LSTM / GRU

Classic combo.

• CNN: short-term motion patterns

• LSTM/GRU: rhythm, continuity

Downsides:

• More hyperparameter pain

• Easier to overfit

• Heavier for on-device use

Worth it if you have enough data and care a lot about sequence context.

3) “Small datasets / explainability / transitions”

* HMM (or classical ML + HMM)

Unfashionable, but still legit.

• Explicit state transitions (walking → running, not teleporting)

• Works well for smoothing predictions

• Still common in rehab / medical setups

For handling activity transitions, HMMs can actually beat deep models.

4) “What about Transformers?”

* Mostly overrated right now for this use case.

• Needs tons of data

• Higher latency

• Overkill for wearable devices

Great for papers and benchmarks. Questionable for products.

A practical progression that doesn’t burn you out

If I were answering this on Reddit:

Don’t chase SOTA on day one.
Chase something that survives contact with real users.

A sane pipeline:

1. Simple rule-based baseline (variance, FFT energy)

2. 1D-CNN

3. CNN + GRU

4. Optional HMM post-processing for smoothing

Always check:

• Per-user normalization

• Confusion matrix

Because almost every system dies here:

• walking ↔ stairs

• fast walking ↔ slow running