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Dataset

HLS-HAR was designed for long-session sports recording rather than isolated window classification. Huawei sports watches captured wrist acceleration and angular velocity in real sports-health monitoring sessions. Research access is coordinated by the Hainan University organizers.

137recordings
46.8Mvalid samples
259.6 hsensor data
5activities

Request research access

HLS-HAR participant recordings are not distributed on GitHub. Before the planned PhysioNet release, researchers can submit the dataset request form. The Hainan University data organizer reviews each request.

Submit a data request Read the access instructions

Activities

The foreground vocabulary contains badminton, rope skipping, dumbbell fly, running, and table tennis. Background motion is modeled internally but is not reported as a workout record.

Activity External-test segments
Badminton 32
Rope skipping 20
Dumbbell fly 20
Running 20
Table tennis 22
Total 114

Splits

The split is fixed at the recording level.

Split Recordings Use
Training 80 model fitting and training-stage selection
Development / calibration 20 temporal-policy tuning and diagnostics
Independent external test 37 final scoring after the operating point is frozen

The 100-record model-development pool contains 284 labeled activity segments, about 34.8 million samples, and 130.5 hours of sensing. The 80-record training split contributes about 28.2 million samples and 113.5 hours.

Development-set scores are diagnostics, not independent estimates. External labels are used only for final scoring; they are not used to select checkpoints, fusion rules, TRL parameters, or reported variants.

Signals

The reported system uses six 100 Hz channels:

Signal Meaning
\(a_x, a_y, a_z\) tri-axial wrist acceleration
\(\omega_x, \omega_y, \omega_z\) tri-axial wrist angular velocity
timestamp millisecond reference for segment boundaries

Three overlapping views are constructed with a one-second step:

Window Samples Training windows
3 s 300 281,871
5 s 500 281,711
8 s 800 281,471

Labels

Each annotation is an (activity, start, end) segment. Evaluation therefore measures complete records, including event count, boundaries, and duration. Predictions are matched one-to-one with same-class labels at IoU > 0.5.

Scope

Public HAR datasets only partly overlap with this setting. Some use phone accelerometers, short scripted windows, daily activities, or no gyroscope; others do not provide long-session segment records. Cross-paper window accuracy is therefore not directly comparable with HLS-HAR segment F1.

HAR70+, WISDM-phone, PAMAP2, and OPPORTUNITY are used only for TRL portability checks with dataset-specific models and parameters. They are not leaderboard comparisons and do not establish transfer of the HLS-HAR result.

Access and privacy

Participant recordings are not stored in the public repository. Before the planned PhysioNet release, research requests follow the dataset access instructions. Storage layout and asset checks are documented in Assets.