AI system
- System
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Phantom motor execution: BioPatRec myoelectric pattern recognition (LDA and MLP classifiers) driving augmented/virtual reality and a racing game
- Model
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Linear discriminant analysis and multilayer perceptrons within BioPatRec.
- Release
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Exact release not established.
- Application developer
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Chalmers University of Technology, Biomedical Engineering Division (academic lab; study part-funded by Integrum AB) - Role
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The classifiers converted muscle signals into movements of a virtual limb.
- Configuration
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BioPatRec used one-versus-one linear discriminant analysis and a multilayer perceptron for each degree of freedom.
- Inputs and outputs
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Not assessed
- Autonomy
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Not assessed
- Workflow
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Decodable residual muscle activity in the stump. Classifier-driven virtual limb that responds to the patient's own effort. Repeated sessions with gamified motor execution.
- Human oversight
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Not assessed
- Relation to AI
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The pattern-recognition classifiers turn stump EMG into predicted phantom movements that the virtual limb executes. The authors argue that this engagement of motor circuitry, rather than a mirror of the contralateral limb, is the therapeutic mechanism. So the AI component is central, though the single-case design cannot isolate it.
- AI contribution
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The pattern-recognition classifiers turn stump EMG into predicted phantom movements that the virtual limb executes. The authors argue that this engagement of motor circuitry, rather than a mirror of the contralateral limb, is the therapeutic mechanism. So the AI component is central, though the single-case design cannot isolate it.
Benefits
- Outcome identifiers
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Not assessed
- Benefit
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after ten weeks near-absent pain developing into completely pain-free periods
- Category
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Improved Physical Health
- Status
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Not assessed
- Beneficiary groups
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People. Society.
- Beneficiaries
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Not assessed
- Reach
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Not assessed
- Distribution and access
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Not assessed
- Duration
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Not assessed
- Expected?
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Yes
- Costs and harms
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Not assessed
Measurement
- Studies
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Not assessed
- Study design
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Not assessed
- Measurement
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Not assessed
- Setting and sample
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Not assessed
- Measures
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after ten weeks near-absent pain developing into completely pain-free periods
- Comparison
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the patient's pain after 48 years of conventional treatments (mirror therapy, drugs, acupuncture, hypnosis)
- Results
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Reference: constant burning pain of an intensity of 3 on a scale from 0 to 10 (SF-MGPQ). Result: gradually reduced to complete pain-free periods.
- Additional value from AI
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Not assessed
- Limitations
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Not assessed
- Denominators
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Not assessed
- Materials for replication
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Not assessed
Money
- Kind of value
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Not assessed
- Amount
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Not assessed
- Currency and prices
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Not assessed
- Period and unit
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Not assessed
- Realization
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Not assessed
- Recipients and payers
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Not assessed
- Gross or net
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Not assessed
- Included costs
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Not assessed
- Comparison
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Not assessed
- Method
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Not assessed
- Return and payback
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Not assessed
People and places
- Participants
-
Developer:
Chalmers University of Technology, Biomedical Engineering Division (academic lab; study part-funded by Integrum AB). Operator: Chalmers University of Technology and Sahlgrenska University Hospital, Gothenburg (inferred from author affiliations; the paper does not name the treatment site). - Jurisdictions
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Benefit: Gothenburg,
Sweden (inferred from author affiliations). Developer: Sweden. Operator: Sweden. - Ownership and control
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Not assessed
- Dependencies
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Not assessed
- Cross-border connections
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Not assessed
- Geopolitical implications
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Not assessed
Follow-up
- Conditions for replication
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Patient can produce distinguishable EMG patterns for phantom movements. Weekly or twice-weekly sessions sustained over months. Augmented-reality display of the restored limb.
- Replication attempts
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1 follow-up entries. Benefit recurred: 1.
- Later findings
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2 December 2016. A single-group trial of 14 patients at four clinics in Sweden and Slovenia reported significant reductions in phantom limb pain that held at six-month follow-up.
- Availability
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Not assessed
- Institutional decision
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Not assessed
- Grounds
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Not assessed
- Action and result
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Not assessed
- Success criterion
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Not assessed
Evidence
- Source numbers
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AIBD-0054-source-0001–0003
- Citations
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Numbered references below.
- Language
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Not assessed
- Source relationships
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Developer
- Original evidence
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Not assessed
- Access and versions
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Not assessed
- Supported findings
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Not assessed
- Evidence links
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Not assessed
- Evidence strength
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Not assessed
- Missing evidence
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Not assessed
Grades
- Assessed benefits
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People. Society.
- Depth
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People: 2. Society: 1.
- Reasons
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People: An identifiable patient with 48 years of treatment-resistant pain reached pain-free periods and regained phantom movement. The case gives no long-term follow-up, so a durable life-changing effect is not established. Society: Single case. The same group later ran a 14-patient trial. No population effect is documented.
- Observed or intended
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Not assessed
- Rubric
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Not assessed
- Assessor and date
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Not assessed
- Alternative assessments
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Not assessed
- Fill
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The makers' claim
Entry details
- Kind
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Not assessed
- Related entries
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Not assessed
- Field
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Rehabilitation medicine / chronic pain. Upper-limb amputees with phantom limb pain.
- Purpose
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Treat phantom limb pain by letting the patient execute phantom movements decoded from stump muscle activity
- Dates
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Date basis unassessed: 2013 (precision unassessed). Updated: 21 October 2026.
- Stage
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Not assessed
- Version
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SHA-256: 137884106506…
References
- Other identifiers
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Not assessed
- Category mappings
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Not assessed
- Brief
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Not assessed
- Revisions
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6 revisions. Latest: 21 October 2026.
Sources
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Peer-reviewed study Developer
Ortiz-Catalan M, Guðmundsdóttir RA, Kristoffersen MB, et al. (2016). Phantom motor execution facilitated by machine learning and augmented reality as treatment for phantom limb pain: a single group, clinical trial in patients with chronic intractable phantom limb pain. Lancet 388(10062):2885-2894.
Phantom limb pain decreased from pre-treatment to the last treatment session by 47% (SD 39; absolute mean change 1·0 [0·8]; p=0·001) for weighted pain distribution
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Case report Developer
Ortiz-Catalan M, Sander N, Kristoffersen MB, Håkansson B, Brånemark R (2014). Treatment of phantom limb pain (PLP) based on augmented reality and gaming controlled by myoelectric pattern recognition: a case study of a chronic PLP patient. Frontiers in Neuroscience 8:24.
The sustained level of pain reported by the patient was gradually reduced to complete pain-free periods.
Individual and simultaneous phantom movements were predicted using myoelectric pattern recognition and were then used as input for VR and AR environments, as well as for a racing game.