# matghazaryan/SOMotionDetector

Simple library to detect motion type (walking, running, automotive) and count users steps. This library will make motion detection much more easily.

Repository: https://github.com/matghazaryan/SOMotionDetector
Canonical: https://ross.abutalabs.com/products/somotiondetector
Language: Objective-C
License: MIT
License Family: permissive
Last push: 2024-08-31T02:27:26+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 4562, "days_push": 733, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1057, forks 145 (observed 2026-08-28T04:03:24.884157+00:00)

## What it is
An Objective-C library for iOS that detects user motion type (not moving, walking, running, automotive) and counts steps using CoreLocation and CoreMotion data. It works on all iOS devices without requiring the M7 coprocessor chip.

## Use cases
- detect whether the user is walking, running, or driving in an iOS app
- count user steps with a pedometer library
- track motion activity in the background and relaunch the app from killed state
- build a fitness tracking feature without needing the M7 chip
- get callbacks when motion type or location changes
- customize speed thresholds for walking, running, and automotive detection

## When to choose
- you need motion type detection on older iOS devices without an M7 coprocessor
- you want a simple drop-in Objective-C library with block-based callbacks
- you need background location updates and app relaunch from killed state
- you want customizable speed thresholds for activity classification

## When to avoid
- you are building for Android or cross-platform with a single codebase
- you need modern Swift-native APIs like CoreMotion's CMMotionActivityManager
- your minimum deployment target is above iOS 9 and you want actively maintained Swift alternatives
- you need highly accurate activity recognition that dedicated hardware sensors provide

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning
- domain: mobile-development
- platform: -
- tags: motion-detection, step-counting, coremotion, corelocation, pedometer, activity-recognition, sensor-fusion, ios, objective-c

## Member repositories
- matghazaryan/SOMotionDetector (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:24.884157+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:57:59.286814+00:00, confidence not recorded.
  - readme: https://github.com/matghazaryan/SOMotionDetector (fetched 2026-08-28T04:03:24.884157+00:00, sha 1d6a8592da9e)
- Data as of 2026-08-30T08:39:29.467469+00:00.