HumanFallDetection: an LSTM fall classifier layered on openpifpaf pose estimation

The gap it fills between pose estimation and a fall label

Pose estimators such as openpifpaf give you skeletons per frame. They do not give you an event. A skeleton that drops from standing height to floor level over a few hundred milliseconds is a fall; the same skeleton crouching deliberately is not. HumanFallDetection sits in that gap. The README states that the project augments openpifpaf with multi-camera and multi-person tracking and adds an LSTM network that predicts two classes, Fall or No Fall. The intended reader is someone who already has video, already accepts pose estimation as the front end, and wants the classification layer rather than a full product. The citation points to a 2021 SPIE Medical Imaging paper, so the primary audience is researchers reproducing or extending that work, not integrators looking for a packaged detector.

What the pipeline actually does, as far as the repository states it

The data flow is described in two sentences of the README. Poses come out of openpifpaf. From those poses the code extracts five temporal and spatial features. Those five features feed an LSTM classifier that emits one of two labels. Multi-camera support means the same pipeline can consume more than one stream, and multi-person support means more than one track can be classified at once. That is the whole published description. The README does not say what the five features are, how tracks are associated across cameras, or how a person is matched between frames. If you need those details you have to read fall_detector.py, because the repository text does not supply them. The LSTM was trained on the UP-Fall Detection dataset, and the README links a Colab notebook that downloads that dataset and compiles the files into videos, which tells you the expected input shape is video files, not a live protocol.

Running it: two commands and four arguments

Setup is a single pip install from the repository root: pip install -r requirements.txt. Execution is python3 fall_detector.py with no arguments required, which starts in the default configuration. The argument table lists four options. num_cams defaults to 1 and sets how many cameras or videos are processed. video defaults to None, and None means capture live video from the camera or cameras. For a single video you save it as abc.xyz and pass --video=abc.xyz. For two videos the naming convention is abc1.xyz and abc2.xyz on disk, with --video=abc.xyz on the command line, so the numeric suffix is appended by the loader rather than typed by you. save_output defaults to False; when enabled, output videos land in the same directory as the input with out prepended to the filename. disable_cuda defaults to False and forces frame processing onto the CPU. The defaults matter: run it with no flags and it will try to open a live camera and will not write a file, which is a confusing first experience if your intent was to test on a clip.

Where it breaks: missing evaluation, dataset coupling and occlusion

The README reports no accuracy, no false-positive rate and no latency figure. For a fall detector that is the number that decides whether the tool is usable, and its absence means you cannot compare this project against anything without running your own evaluation. The model was trained on UP-Fall Detection, a specific dataset with its own camera placement and its own definition of what counts as a fall. Five spatial and temporal features learned on that geometry will not transfer unchanged to a ceiling-mounted fisheye or a handheld phone. Multi-person tracking also has an obvious failure mode that the repository does not address: when two people cross, or when one person is occluded by furniture, identity assignment can swap, and an LSTM reading a swapped track sees a trajectory that never happened. The README is silent on how that is handled. Finally, this is a two-class classifier, Fall or No Fall. It has no notion of a near-fall, a sit-down, or a person lying on the floor for unrelated reasons, and it is not described as producing a confidence score you could threshold.

How it differs from MediaPipe-based fall scripts and from dedicated fall datasets

The common alternative in this space is a MediaPipe Pose script that computes a body aspect ratio or a bounding-box height threshold per frame and raises an alarm when the ratio crosses a line. That approach needs no training data and no model file, and it fails on the same occlusion cases but for a different reason: it has no temporal memory at all, so a single bad frame is a single bad decision. HumanFallDetection makes the opposite trade. It adds an LSTM, which means it needs the UP-Fall Detection data, a training step and the Colab notebook the README links, and in exchange it can in principle learn motion over time rather than a static posture. The other comparison point is the UP-Fall Detection dataset itself, which ships baseline classifiers. Using those baselines means staying inside the dataset authors' pipeline; using this repository means taking the openpifpaf pose front end and the multi-camera argument handling along with the classifier. If your cameras are fixed and your fall definition matches UP-Fall, the difference is small. If not, the LSTM is the part you will be retraining.

Licence, maintenance and the cost of staying current

The repository is MIT licensed, which permits commercial use and modification provided the copyright notice and permission notice are retained. That is a permissive baseline, but it covers this repository's code only. openpifpaf is a separate dependency with its own licence, and the UP-Fall Detection dataset has its own terms that the README does not restate, so check both before shipping anything. The dependency chain is the real maintenance cost. Pose estimation libraries change their output formats and their model weights, and requirements.txt pins whatever version worked when the paper was written. There are no releases in the repository history, so upgrades arrive as commits on master with no changelog to read. The paper is from 2021 and the last push is dated 2026, so the code has been touched recently, but a recent push is not the same as a maintained dependency set. Budget time for the openpifpaf upgrade before you budget time for anything else.

Editorial conclusion

Adopt it if you want a readable reference implementation of pose-feature fall classification and you accept that the shipped model is tied to the UP-Fall Detection setup, so you will retrain on your own camera geometry before trusting it. Do not adopt it as a drop-in safety system for a care home or a ward: the README documents no accuracy figures, no latency budget and no failure handling, and the five features it extracts are not described anywhere in the repository text. Before anything else, check the requirements.txt pins against your CUDA build, run python3 fall_detector.py --video=abc.xyz on one clip with --save_output to confirm the out-prefixed file appears, and inspect the pose output on your own footage to see whether openpifpaf holds a track through an occlusion.

Official sources

1. Issues
2. License: MIT
3. README
4. taufeeque9/HumanFallDetection on GitHub