Human Activity Recognition (HAR) is also known as / has sub-tasks such as
- Activity Recognition / human activity recognition (AR)
- Activities of Daily Living (ADL).
- Action recognition
- Fall detection. (FD)
- Fitness tracking / Exercise recognition
- Sleep monitoring / sleep quality tracking
- Posture monitoring
- Gait analysis
- Eating monitoring
- Seizure detection
Giving some overview of the area.
https://www.nature.com/articles/s41746-021-00514-4
Marcin Straczkiewicz, Peter James & Jukka-Pekka Onnela. 2021
Describes the various approaches used for data acquisition, data preprocessing, feature extraction, and activity classification, identifying the most common practices, and their alternatives.
Very nice illustrations.
- UniMiB SHAR 11,771 samples of human activities and falls. 30 subjects, aged 18 to 60 years. 17 fine grained classes grouped in two coarse grained classes. 9 types of activities of daily living (ADL), 8 types of falls.
- UCI: ADL Recognition with Wrist-worn Accelerometer. 16 volunteers performing 14 Activities of Daily Living Classes: (brush_teeth, climb_stairs, comb_hair, descend_stairs, drink_glass, eat_meat, eat_soup, getup_bed, liedown_bed, pour_water, sitdown_chair, standup_chair, use_telephone, walk).
- UCI: Activity Recognition from Single Chest-Mounted Accelerometer. 15 participantes performing 7 activities. 52Hz. Working at Computer, Standing Up/Walking/Going up\down stairs, Standing, Walking, Going Up\Down Stairs, Walking and Talking with Someone, Talking while Standing
- PAMAP2 Physical Activity Monitoring Data Set. 100 Hz, 3 IMUs: wrist,chest,ankle. Heartrate 9Hz. 18 physical activities, performed by 9 subjects
- LingAcceleration. 20 activities, 20 subjects
- UCI-DSADS: UCI Daily and Sports Activities. 19 daily and sports activities. 8 subjects in their own style. 5 minutes per parcicipant. Five Xsens MTx units are used on the torso, arms, and legs.
- UCI: Smartphone-Based Recognition of Human Activities and Postural Transitions Data Set. Smartphone (Samsung Galaxy S II) on the waist. 30 volunteers age 19-48 years. Six basic activities. Preprocessed into 2.56 sec sliding windows with 50% overlap (128 readings/window), time+frequency based features. Total 561 features, 10k instances.
- UCI: OPPORTUNITY Activity Recognition Data Set Scripted execution with 4 users, 6 runs per user. 7 IMUs plus bunch of other sensors on body and around. 5 tracks of labels. 242 features, 2551 instances.
- WISDM: Activity prediction in lab conditions. Raw set 6 features, 1M instances, 6 classes. Preprocessed set 46 fetures, 5k instances.
- WISDM: Actitracer, real world data. 0.5% labeled data, rest unlabeled. 500 users. Available both as raw motion and preprocessed. Preprocessed data has 5k labeled classes. 6 basic classes.
- HAR-CNN-Keras-STM32. Subset of WISDM classes, collected on a SensorTile.
- Exercise Recognition from Wearable Sensors dataset. Arm-worn inertial sensor. Triaxial accelerometer plus gyro. 13 exercises. 114 participants over 146 sessions. Stored in Matlab .m file.
- MM-Fit. 2 smartwatches, 2 smartphones, 1 earbud, 1 camera. 10 exercises. 800 minutes.
- Capture-24: Activity tracker dataset for human activity recognition. Axivity AX3 wrist-worn activity tracker. 151 participants Around 24 hours, total of almost 4,000 hours. More than 2,500 hours of labelled data. Academic Use Licence 1.1. Non-commercial.
- motion sense. Smartphone in trouser pocket. 6 activities. 24 data subjects.
- UCI-HAR. Human Activity Recognition Using Smartphones. 30 volunteers within an age bracket of 19-48 years. Six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. Split into of 2.56 sec and 50% overlap (128 readings/window). Pre-computed features also available. NOTE: replaced by http://archive.ics.uci.edu/dataset/341/smartphone+based+recognition+of+human+activities+and+postural+transitions, which has original time-series.
- HARTH: A Human Activity Recognition Dataset for Machine Learning. Twenty-two participants. Recorded for 90 to 120 min during their regular working hours. Using two three-axial accelerometers, attached to the thigh and lower back. Sampling rate of 50 Hz. Paper published in MDPI Sensors. Hosted by NTNU on Github. Researchers from various universities in Norway.
- w-HAR: An Activity Recognition Dataset and Framework Using Low-Power Wearable Devices. 22 user subjects. IMU (accelerometer and gyroscope) and stretch sensor data. Performed activities in the classes: jump, lie down, sit, stand, stairs down, stairs up, walk.
- DU-MD: An Open-Source Human Action Dataset for Ubiquitous Wearable Sensors. Single wrist-mounted wearable sensor. 25 subjects. 10 Activities of Daily Life, 7 basic ADL and 3 falls. 2500 segments total. Download.
- USC-HAD: A Daily Activity Dataset for Ubiquitous Activity Recognition Using Wearable Sensors. Download
- Efficient Activity Recognition and Fall Detection
- Limitations with Activity Recognition Methodology & Data Set. Focuses on model type, how AR training and test data are partitioned, and how AR models are evaluated. personal, hybrid, and impersonal/universal models yield dramatically different performance.
- Transfer Learning for Activity Recognition: A Survey. Summarizes 30+ papers using transfer learning.
- RecoFit: using a wearable sensor to find, recognize, and count repetitive exercises. 2014. Handles 3 tasks: 1) segmenting exercise from intermittent non-exercise periods, (2) recognizing which exercise is being performed, and (3) counting repetitions.
- Towards a Complete Set of Gym Exercises Detection Using Smartphone Sensors. Reviewed 25 studies between 2006–2018 6 of 25 research studies related to gym exercises, while the remaining 19 of 25 research papers were about daily life physical activities, emotional recognition, and elderly fall detection
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0337897 2025.
The raw data was converted into four common metrics: Movement Acceleration Intensity (MAI), Euclidean Norm Minus One (ENMO), Mean Amplitude Deviation (MAD) and counts per minute (CPM). Correlations between MAI, MAD and ENMO were high (r = .9), while correlations between CPM and other metrics were substantially lower (r = .78), less linear, and influenced by activity type.
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0261718 2021.
Actigraphic measurements are an important part of research in different disciplines, yet the procedure of determining activity values is unexpectedly not standardized in the literature. We assessed the different methods by analysing the triaxial acceleration signals measured during a 10-day movement of 42 subjects. We calculated 148 different activity signals for each subject’s movement using the combinations of various types of preprocessing and 7 different activity metrics applied on both axial and magnitude data.
Validation of Cut-Points for Evaluating the Intensity of Physical Activity with Accelerometry-Based Mean Amplitude Deviation (MAD)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0134813 2015
Derive a regression model that estimates oxygen consumption (VO2) from MAD values and validate the MAD-based cut-points for light, moderate and vigorous locomotion against VO2 within a wide range of speeds. The MAD and VO2 showed a very strong association. Within individuals, the range of r values was from 0.927 to 0.991 providing the mean r = 0.969. The optimal MAD cut-point for 3.0 MET was 91 mg (milligravity) and 414 mg for 6.0 MET.
A “one-size-fits-most” walking recognition method for smartphones, smartwatches, and wearable accelerometers
https://www.nature.com/articles/s41746-022-00745-z
https://pubmed.ncbi.nlm.nih.gov/37336086/
Humans naturally transition from walking to running at a point known as the walk-to-run transition (WRT). The WRT commonly occurs at a speed of ∼2.1 m/s (m/s) or a Froude number (dimensionless value considering leg length) of 0.5. Emerging evidence suggests the WRT can also be classified using a cadence of 140 steps/min.