{ "id": "AIBD-0055", "title": "Machine-learning myoelectric motor execution partially restores thumb and wrist movement in a paralyzed replanted arm", "summary": "A 72-year-old with a paralyzed replanted arm regained active thumb and wrist movement after 18 sessions of ML-decoded motor execution.", "date_observed": "2024-11-23", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "A 72-year-old man's right arm was replanted after a traumatic above-elbow amputation. He had no functional recovery after conventional rehabilitation and developed chronic neuropathic pain. The arm had been replanted at Sahlgrenska University Hospital in Mölndal, Sweden. The Mölndal-based Center for Bionics and Pain Research / Sahlgrenska University Hospital (C.A.R.E.) team then delivered 18 sessions of myoelectric motor execution over 84 days. Nine included sensory training with tactile grids (the paper does not name the treatment site). Eight channels of electromyography were classified by a linear discriminant analysis classifier (Neuromotus, Integrum AB). Intended movements were decoded from the replanted arm and shown on screen as virtual limbs and games. He regained active thumb extension (4 degrees) and active wrist flexion (6 degrees) and extension (10 degrees), movements absent before treatment. Pain fluctuated with no consistent change. Treatment dates are not given, so the publication date (23 November 2024) is used.", "category": "benefit created", "unexpected": false, "purpose": "Rehabilitate a highly impaired replanted arm and reduce neuropathic pain using decoded motor intention as feedback", "system": "Myoelectric motor execution with sensory training: Neuromotus 8-channel EMG and LDA classifier driving virtual limbs and serious games", "model_version": "Neuromotus (Integrum AB) with linear discriminant analysis classifier", "developer": "Center for Bionics and Pain Research / University of Gothenburg (academic clinical team); device by Integrum AB", "deployer": "Center for Bionics and Pain Research and Sahlgrenska University Hospital (C.A.R.E.), Mölndal; treatment site not named in the paper", "location": "Mölndal, Sweden", "domain": "Rehabilitation medicine / hand surgery", "segment": "Patients with replanted or transplanted limbs", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 2, "reason": "An identifiable patient regained active thumb and wrist movement conventional rehabilitation had not produced. Gains are small and pain did not consistently improve." }, { "group": "societal", "depth": 1, "reason": "Single case; authors present it as a potential treatment needing further study." } ], "ai_role": { "status": "assessed", "statement": "The LDA classifier decodes intended movements from weak EMG in a hand and wrist with no active motion. That is the only way the patient could receive real-time feedback on his motor attempts. The authors attribute the regained movement to this motor execution training. Two authors hold a pending patent on the technology." }, "evidence": [ { "kind": "case report", "citation": "Kristoffersen MB, Munoz-Novoa M, Buist M, Emadeldin M, Reinholdt C, Ortiz-Catalan M (2024). Myoelectric motor execution and sensory training to treat chronic pain and paralysis in a replanted arm: a case study. Journal of NeuroEngineering and Rehabilitation 21:204.", "title": "Myoelectric motor execution and sensory training to treat chronic pain and paralysis in a replanted arm: a case study", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11585237/", "date": "2024-11-23", "written_by": "makers", "quote": "The participant regained active extension of the thumb (4 degrees) and regained active wrist movement (flex: 6 degrees, extend: 10 degrees)" }, { "kind": "case report", "citation": "Kristoffersen MB, et al. (2024). J NeuroEng Rehabil 21:204 (methods).", "title": "Myoelectric motor execution and sensory training to treat chronic pain and paralysis in a replanted arm: a case study", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11585237/", "date": "2024-11-23", "written_by": "makers", "quote": "intended movements were decoded from the replanted arm's myoelectric signals using machine learning and real-time feedback was provided on a screen" }, { "kind": "peer-reviewed study", "citation": "Munoz-Novoa M, Kristoffersen MB, Sunnerhagen KS, Naber A, Ortiz-Catalan M, Alt Murphy M (2025). Myoelectric pattern recognition with virtual reality and serious gaming improves upper limb function in chronic stroke: a single case experimental design study. Journal of NeuroEngineering and Rehabilitation 22:6.", "title": "Myoelectric pattern recognition with virtual reality and serious gaming improves upper limb function in chronic stroke: a single case experimental design study", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11742229/", "date": "2025-01-17", "written_by": "makers", "quote": "All participants improved upper limb function assessed by FMA-UE (Tau-U 0.72-1.0), and five out of six improved beyond the minimal clinical important difference (MCID)." } ], "replication": { "produced_by": [ "Residual EMG detectable in the impaired limb", "Classifier trained on the patient's own signals each session", "Repeated sessions with virtual-limb and game feedback" ], "must_hold": [ "Intact or reinnervated muscle producing separable EMG patterns", "Clinician-run sessions several times a week", "Patient motivation to attempt movements without visible motion" ], "echoes": [ { "date": "2025-01-17", "setting": "Six people with chronic stroke, 18 sessions of myoelectric pattern recognition with VR and gaming (single-case experimental design)", "model_version": "Neuromotus MPR with VR", "outcome": "recurred", "source": "Munoz-Novoa et al. (2025) J NeuroEng Rehabil, PMID 39825410" } ] }, "paired_incident": null, "headline": "Neuromotus EMG decoding restores thumb and wrist movement", "metric": "active thumb extension (4 degrees) and active wrist flexion (6 degrees)", "comparator": { "against": "the arm's function before treatment, after conventional rehabilitation", "before": "no active movement prior the MME interventions", "after": "active extension of the thumb (4 degrees) and regained active wrist movement (flex: 6 degrees, extend: 10 degrees)", "basis": "measured", "quote": "regained active extension of the thumb (4 degrees) and regained active wrist movement (flex: 6 degrees, extend: 10 degrees), both of which had no active movement prior the MME interventions", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11585237/" }, "later": { "status": "unknown", "date": null, "note": "No follow-up beyond the final session is reported.", "quote": null, "url": null }, "value": null, "developer_country": "Sweden", "deployer_country": "Sweden", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Reworded the account and deployer so that Sahlgrenska University Hospital is credited only with the replantation surgery because the paper never says where the MME sessions were delivered; corrected the article numbers to 21:204 and 22:6; qualified the title and the system's part to “partially restores” and “a hand and wrist with no active motion” to match the paper's own limits; and restored the full source title on evidence 2.", "source": "Kristoffersen et al. (2024) J NeuroEng Rehabil 21:204, PMC11585237; Munoz-Novoa et al. (2025) J NeuroEng Rehabil 22:6, PMID 39825410", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11585237/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=AFF:%22Center%20for%20Bionics%20and%20Pain%20Research%22&resultType=core&format=json&pageSize=5; https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=AFF:%22Center%20for%20Bionics%20and%20Pain%20Research%22&resultType=core&format=json&pageSize=5", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The makers' claim", "claim_basis": "Sources: the makers' peer-reviewed study and the makers' case report. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0055", "catalog_version": "1.0.0", "source_fingerprint": "8f82e8810e839ac74311e0f5f101a9da2d8dddc3e91c79bb8d43683fb0936c01", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0055-source-0001", "AIBD-0055-source-0002", "AIBD-0055-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Center for Bionics and Pain Research / University of Gothenburg (academic clinical team); device by Integrum AB", "deployer": "Center for Bionics and Pain Research and Sahlgrenska University Hospital (C.A.R.E.), Mölndal; treatment site not named in the paper" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The LDA classifier decodes intended movements from weak EMG in a hand and wrist with no active motion. That is the only way the patient could receive real-time feedback on his motor attempts. The authors attribute the regained movement to this motor execution training. Two authors hold a pending patent on the technology." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The LDA classifier decodes intended movements from weak EMG in a hand and wrist with no active motion. That is the only way the patient could receive real-time feedback on his motor attempts. The authors attribute the regained movement to this motor execution training. Two authors hold a pending patent on the technology." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "An identifiable patient regained active thumb and wrist movement conventional rehabilitation had not produced. 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The cited evidence includes no independent source and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "An identifiable patient regained active thumb and wrist movement conventional rehabilitation had not produced. 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"record_fields", "fields": { "benefit_type": "Improved Physical Health" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "A 72-year-old man's right arm was replanted after a traumatic above-elbow amputation. He had no functional recovery after conventional rehabilitation and developed chronic neuropathic pain. The arm had been replanted at Sahlgrenska University Hospital in Mölndal, Sweden. The Mölndal-based Center for Bionics and Pain Research / Sahlgrenska University Hospital (C.A.R.E.) team then delivered 18 sessions of myoelectric motor execution over 84 days. Nine included sensory training with tactile grids (the paper does not name the treatment site). Eight channels of electromyography were classified by a linear discriminant analysis classifier (Neuromotus, Integrum AB). Intended movements were decoded from the replanted arm and shown on screen as virtual limbs and games. He regained active thumb extension (4 degrees) and active wrist flexion (6 degrees) and extension (10 degrees), movements absent before treatment. Pain fluctuated with no consistent change. Treatment dates are not given, so the publication date (23 November 2024) is used.", "benefit_type": "Improved Physical Health" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "An identifiable patient regained active thumb and wrist movement conventional rehabilitation had not produced. Gains are small and pain did not consistently improve." }, { "group": "societal", "depth": 1, "reason": "Single case; authors present it as a potential treatment needing further study." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Rehabilitate a highly impaired replanted arm and reduce neuropathic pain using decoded motor intention as feedback" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Mölndal, Sweden", "developer_country": "Sweden", "deployer_country": "Sweden" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Rehabilitate a highly impaired replanted arm and reduce neuropathic pain using decoded motor intention as feedback" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2024-11-23", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Reworded the account and deployer so that Sahlgrenska University Hospital is credited only with the replantation surgery because the paper never says where the MME sessions were delivered; corrected the article numbers to 21:204 and 22:6; qualified the title and the system's part to “partially restores” and “a hand and wrist with no active motion” to match the paper's own limits; and restored the full source title on evidence 2.", "source": "Kristoffersen et al. (2024) J NeuroEng Rehabil 21:204, PMC11585237; Munoz-Novoa et al. 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J NeuroEng Rehabil 21:204 (methods).", "title": "Myoelectric motor execution and sensory training to treat chronic pain and paralysis in a replanted arm: a case study", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11585237/", "date": "2024-11-23", "written_by": "makers", "quote": "intended movements were decoded from the replanted arm's myoelectric signals using machine learning and real-time feedback was provided on a screen" }, { "kind": "peer-reviewed study", "citation": "Munoz-Novoa M, Kristoffersen MB, Sunnerhagen KS, Naber A, Ortiz-Catalan M, Alt Murphy M (2025). Myoelectric pattern recognition with virtual reality and serious gaming improves upper limb function in chronic stroke: a single case experimental design study. Journal of NeuroEngineering and Rehabilitation 22:6.", "title": "Myoelectric pattern recognition with virtual reality and serious gaming improves upper limb function in chronic stroke: a single case experimental design study", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11742229/", "date": "2025-01-17", "written_by": "makers", "quote": "All participants improved upper limb function assessed by FMA-UE (Tau-U 0.72-1.0), and five out of six improved beyond the minimal clinical important difference (MCID)." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0055-source-0001", "AIBD-0055-source-0002", "AIBD-0055-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "case report", "citation": "Kristoffersen MB, Munoz-Novoa M, Buist M, Emadeldin M, Reinholdt C, Ortiz-Catalan M (2024). 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J NeuroEng Rehabil 21:204 (methods).", "title": "Myoelectric motor execution and sensory training to treat chronic pain and paralysis in a replanted arm: a case study", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11585237/", "date": "2024-11-23", "written_by": "makers", "quote": "intended movements were decoded from the replanted arm's myoelectric signals using machine learning and real-time feedback was provided on a screen" }, { "kind": "peer-reviewed study", "citation": "Munoz-Novoa M, Kristoffersen MB, Sunnerhagen KS, Naber A, Ortiz-Catalan M, Alt Murphy M (2025). Myoelectric pattern recognition with virtual reality and serious gaming improves upper limb function in chronic stroke: a single case experimental design study. Journal of NeuroEngineering and Rehabilitation 22:6.", "title": "Myoelectric pattern recognition with virtual reality and serious gaming improves upper limb function in chronic stroke: a single case experimental design study", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11742229/", "date": "2025-01-17", "written_by": "makers", "quote": "All participants improved upper limb function assessed by FMA-UE (Tau-U 0.72-1.0), and five out of six improved beyond the minimal clinical important difference (MCID)." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "A 72-year-old with a paralyzed replanted arm regained active thumb and wrist movement after 18 sessions of ML-decoded motor execution.", "account": "A 72-year-old man's right arm was replanted after a traumatic above-elbow amputation. He had no functional recovery after conventional rehabilitation and developed chronic neuropathic pain. The arm had been replanted at Sahlgrenska University Hospital in Mölndal, Sweden. The Mölndal-based Center for Bionics and Pain Research / Sahlgrenska University Hospital (C.A.R.E.) team then delivered 18 sessions of myoelectric motor execution over 84 days. Nine included sensory training with tactile grids (the paper does not name the treatment site). Eight channels of electromyography were classified by a linear discriminant analysis classifier (Neuromotus, Integrum AB). Intended movements were decoded from the replanted arm and shown on screen as virtual limbs and games. He regained active thumb extension (4 degrees) and active wrist flexion (6 degrees) and extension (10 degrees), movements absent before treatment. Pain fluctuated with no consistent change. Treatment dates are not given, so the publication date (23 November 2024) is used." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The classifier decoded intended movements from muscle signals and controlled a virtual limb." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "The Neuromotus system used a classifier based on linear discriminant analysis." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "value_present", "value": { "kind": "text", "text": "A classifier based on linear discriminant analysis." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "Neuromotus (Integrum AB) with linear discriminant analysis classifier" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Myoelectric motor execution with sensory training: Neuromotus 8-channel EMG and LDA classifier driving virtual limbs and serious games" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Center for Bionics and Pain Research / University of Gothenburg (academic clinical team); device by Integrum AB" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Machine-learning myoelectric motor execution partially restores thumb and wrist movement in a paralyzed replanted arm", "headline": "Neuromotus EMG decoding restores thumb and wrist movement" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "8f82e8810e839ac74311e0f5f101a9da2d8dddc3e91c79bb8d43683fb0936c01" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "A 72-year-old man's right arm was replanted after a traumatic above-elbow amputation. He had no functional recovery after conventional rehabilitation and developed chronic neuropathic pain. The arm had been replanted at Sahlgrenska University Hospital in Mölndal, Sweden. The Mölndal-based Center for Bionics and Pain Research / Sahlgrenska University Hospital (C.A.R.E.) team then delivered 18 sessions of myoelectric motor execution over 84 days. Nine included sensory training with tactile grids (the paper does not name the treatment site). Eight channels of electromyography were classified by a linear discriminant analysis classifier (Neuromotus, Integrum AB). Intended movements were decoded from the replanted arm and shown on screen as virtual limbs and games. He regained active thumb extension (4 degrees) and active wrist flexion (6 degrees) and extension (10 degrees), movements absent before treatment. Pain fluctuated with no consistent change. Treatment dates are not given, so the publication date (23 November 2024) is used.", "replication": { "produced_by": [ "Residual EMG detectable in the impaired limb", "Classifier trained on the patient's own signals each session", "Repeated sessions with virtual-limb and game feedback" ], "must_hold": [ "Intact or reinnervated muscle producing separable EMG patterns", "Clinician-run sessions several times a week", "Patient motivation to attempt movements without visible motion" ], "echoes": [ { "date": "2025-01-17", "setting": "Six people with chronic stroke, 18 sessions of myoelectric pattern recognition with VR and gaming (single-case experimental design)", "model_version": "Neuromotus MPR with VR", "outcome": "recurred", "source": "Munoz-Novoa et al. (2025) J NeuroEng Rehabil, PMID 39825410" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 33, "reviewed": 0, "record_mapped": 33, "not_assessed": 58, "complete": false, "by_state": { "value_present": 33, "not_reported": 0, "unresolved": 0, "not_assessed": 58, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } }