{"dataset":{"id":"52","dataset_id":"nm000107","name":"FRL Wrist Control: Wrist Movement Decoding from Surface Electromyography","description":"NEMAR Dataset nm000107: wrist - Wrist movement control from EMG","owner_user_id":2,"status":"active","github_repo":"nemarDatasets/nm000107","concept_doi":"10.82901/nemar.nm000107","latest_version_doi":"10.82901/nemar.nm000107.v2.0.0","created_at":"2026-01-19 03:25:23","updated_at":"2026-07-10 21:43:08","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"FRL Wrist Control: Wrist Movement Decoding from Surface Electromyography\",\n  \"description\": \"This dataset comprises wrist-based surface electromyography (sEMG) recordings from 100 participants performing continuous cursor control via wrist flexion and extension movements. Synchronized motion capture provides ground-truth wrist angles, enabling evaluation of sEMG-based decoding of motor intent. The study demonstrates the feasibility of gesture-free, non-invasive neuromotor interfaces for human-computer interaction, with applications to AR/VR navigation and assistive control systems. Note: Raw data contains duplicate timestamps and irregular sampling in many sessions; post-processing includes duplicate removal and resampling to regular 2000 Hz intervals.\",\n  \"methods_description\": \"Participants wore a 16-channel sEMG wristband (sEMG-RD) on the dominant wrist while performing a target acquisition task involving continuous horizontal cursor control via wrist flexion/extension. Motion capture tracked wrist angles in real-time at 2000 Hz sampling rate using bipolar differential referencing. Participants navigated to visual targets and held position for 500ms. Data were post-processed to remove duplicate timestamps and resample to regular 2000 Hz intervals to address data quality issues.\",\n  \"license\": \"CC-BY-NC 4.0\",\n  \"authors\": {\n    \"Patrick Kaifosh\": {},\n    \"Thomas R. Reardon\": {},\n    \"CTRL-labs at Reality Labs\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"Electromyography\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004576\"\n    },\n    {\n      \"term\": \"motor control\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D009048\"\n    },\n    {\n      \"term\": \"wrist movement\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D014953\"\n    },\n    {\n      \"term\": \"neural decoding\"\n    },\n    {\n      \"term\": \"human-computer interaction\"\n    },\n    {\n      \"term\": \"sEMG\"\n    },\n    {\n      \"term\": \"cursor control\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1038/s41586-025-09255-w\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000107\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=nm000107\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.5281/zenodo.17613963\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsIdenticalTo\"\n    },\n    {\n      \"identifier\": \"10.82901/nemar.nm000107\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsIdenticalTo\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"Meta Reality Labs\"\n    }\n  ],\n  \"resource_type_specific\": \"EMG Dataset\",\n  \"modalities\": [\n    \"emg\"\n  ],\n  \"source_hash\": \"a09eb232045041f8dc112c2f97131b23d87b607346e5a9a815e8d1389fb7dfdc\"\n}","last_activity_at":"2026-02-12 09:49:28","source":null,"source_id":null,"subject_count":100,"modalities":"emg","age_min":null,"age_max":null,"file_size":26684658756,"total_files":922,"tasks":"wrist","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Patrick Kaifosh, Thomas R. Reardon, CTRL-labs at Reality Labs","license":"CC-BY-NC 4.0","readme":"# wrist: Wrist Movement Control from EMG\n\n## Overview\n\n**Dataset**: wrist - Wrist posture and movement from wrist-based surface electromyography\n**Task**: 1D continuous cursor control via wrist flexion/extension\n**Participants**: 100 subjects\n**Sessions**: 100 total (1 per subject)\n**Publication**: Kaifosh et al., 2025 - \"A generic non-invasive neuromotor interface for human-computer interaction\" (Nature)\n\n### Purpose\n\nThis dataset captures wrist-based sEMG signals during wrist movements for continuous cursor control. Motion capture provides ground-truth wrist angles. The goal is to enable gesture-free control through wrist posture alone, demonstrating sEMG's ability to decode motor intent before visible movement occurs.\n\n## Dataset Details\n\n### Participants\n- **Sample size**: 100 participants\n- **Demographics**: Not available (marked as n/a)\n- **Recording side**: Dominant wrist\n- **Sessions**: 1 per participant\n\n### Hardware\n- **Device**: sEMG-RD (single wristband)\n- **Channels**: 16 (EMG0-EMG15)\n- **Sampling rate**: 2000 Hz\n- **Reference**: Bipolar differential\n- **Ground truth**: Motion capture wrist angles\n\n### Recording Protocol\n1. Participant wears sEMG-RD on dominant wrist\n2. Motion capture tracks wrist angles in real-time\n3. Participant controls horizontal cursor position with wrist flexion/extension\n4. Target acquisition task: Navigate to targets and hold for 500ms\n\n## Data Contents\n\n### Files per Session\n```\nsub-XXX/ses-XXX/emg/\n├── sub-XXX_ses-XXX_task-wrist_emg.edf\n├── sub-XXX_ses-XXX_task-wrist_emg.json\n├── sub-XXX_ses-XXX_task-wrist_channels.tsv\n├── sub-XXX_ses-XXX_task-wrist_events.tsv\n└── sub-XXX_ses-XXX_electrodes.tsv\n```\n\n### Events\n- **Stage boundaries**: Task phases and movement trials\n\n### Coordinate System\nSingle coordinate system at root (dominant wrist, percent units, no decimals)\n\n## Signal Processing\n\n**Note**: This dataset has significant data quality issues:\n- Duplicate timestamps found in many sessions (up to 88% duplicates)\n- Irregular sampling requiring resampling (up to 916% deviation)\n- Post-processing: Duplicate removal followed by resampling to regular 2000 Hz\n\n## Baseline Performance\n\n### Published Results (Kaifosh et al., 2025)\n\n**Offline Evaluation**:\n- Wrist angle velocity error: <13°/s\n- Error decreases with more training participants\n\n**Closed-loop Performance** (n=17 naive test users):\n- **Target acquisition time**: Median 1.51s (sEMG decoder)\n- **Dial-in time**: Time to re-acquire after premature exit\n- **Learning effects**: Improvement from practice to evaluation blocks\n\n**Comparison**:\n- Motion capture ground truth: 0.96s\n- MacBook trackpad: 0.68s\n- sEMG decoder: 1.51s (2.2× slower than trackpad)\n\n**Model architecture**: MPF features + LSTM\n\n## Key Findings\n- **Predictive signals**: sEMG precedes movement by tens of milliseconds\n- **Generic models work**: Out-of-the-box cross-user generalization\n- **Continuous control**: Demonstrates feasibility of gesture-free interfaces\n- **Room for improvement**: Performance gap vs traditional inputs\n\n## Use Cases\n- **Continuous control**: Cursor/pointer movement\n- **AR/VR navigation**: Hands-free interface\n- **Low-effort control**: Minimal visible movement required\n- **Predictive decoding**: Intent detection before motion completion\n\n## Known Limitations\n- Single degree of freedom (1D control only)\n- Single wrist (dominant hand)\n- Duplicate timestamps (data quality issue)\n- Performance below traditional inputs\n- Extension to 2D control not demonstrated\n\n## Citation\n```\nKaifosh, P., Reardon, T.R., & CTRL-labs at Reality Labs. 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