Morten B Kristoffersen
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Explore the profile of Morten B Kristoffersen including associated specialties, affiliations and a list of published articles.
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12
Citations
188
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Recent Articles
1.
Munoz-Novoa M, Kristoffersen M, Sunnerhagen K, Naber A, Ortiz-Catalan M, Alt Murphy M
J Neuroeng Rehabil
. 2025 Jan;
22(1):6.
PMID: 39825410
Background: Myoelectric pattern recognition (MPR) combines multiple surface electromyography channels with a machine learning algorithm to decode motor intention with an aim to enhance upper limb function after stroke. This...
2.
Kristoffersen M, Munoz-Novoa M, Buist M, Emadeldin M, Reinholdt C, Ortiz-Catalan M
J Neuroeng Rehabil
. 2024 Nov;
21(1):204.
PMID: 39580427
Background: Following upper limb amputation, surgeries such as arm transplantation or replantation might be an option to restore function. After such surgeries, rehabilitation of the arm is needed. However, conventional...
3.
Lendaro E, Van der Sluis C, Hermansson L, Bunketorp-Kall L, Burger H, Keesom E, et al.
Pain
. 2024 Sep;
166(3):571-586.
PMID: 39250328
Phantom limb pain (PLP) represents a significant challenge after amputation. This study investigated the use of phantom motor execution (PME) and phantom motor imagery (PMI) facilitated by extended reality (XR)...
4.
Bjorkquist A, Guo L, Kristoffersen M, Novoa M, Ortiz-Catalan M, Sandsjo L
Stud Health Technol Inform
. 2023 May;
302:682-683.
PMID: 37203468
This case study reports the use of a new textile-electrode system for self-administered Phantom Motor Execution (PME) treatment at home in one patient with Phantom Limb Pain (PLP). In follow-up...
5.
Munoz-Novoa M, Kristoffersen M, Sunnerhagen K, Naber A, Alt Murphy M, Ortiz-Catalan M
Front Hum Neurosci
. 2022 Jun;
16:897870.
PMID: 35669202
Background: Upper limb impairment is common after stroke, and many will not regain full upper limb function. Different technologies based on surface electromyography (sEMG) have been used in stroke rehabilitation,...
6.
Wand M, Kristoffersen M, Franzke A, Schmidhuber J
IEEE Trans Biomed Eng
. 2022 Jan;
69(7):2283-2293.
PMID: 35007192
Objective: We show that state-of-the-art deep neural networks achieve superior results in regression-based multi-class proportional myoelectric hand prosthesis control than two common baseline approaches, and we analyze the neural network...
7.
Kristoffersen M, Franzke A, Bongers R, Wand M, Murgia A, Van der Sluis C
J Neuroeng Rehabil
. 2021 Feb;
18(1):32.
PMID: 33579326
Background: Upper limb prosthetics with multiple degrees of freedom (DoFs) are still mostly operated through the clinical standard Direct Control scheme. Machine learning control, on the other hand, allows controlling...
8.
Franzke A, Kristoffersen M, Jayaram V, Van der Sluis C, Murgia A, Bongers R
IEEE Trans Neural Syst Rehabil Eng
. 2020 Oct;
29:21-30.
PMID: 33035157
In myoelectric machine learning (ML) based control, it has been demonstrated that control performance usually increases with training, but it remains largely unknown which underlying factors govern these improvements. It...
9.
Kristoffersen M, Franzke A, Van der Sluis C, Bongers R, Murgia A
IEEE Trans Neural Syst Rehabil Eng
. 2020 Aug;
28(9):1977-1983.
PMID: 32746317
Objective: When evaluating methods for machine-learning controlled prosthetic hands, able-bodied participants are often recruited, for practical reasons, instead of participants with upper limb absence (ULA). However, able-bodied participants have been...
10.
Franzke A, Kristoffersen M, Bongers R, Murgia A, Pobatschnig B, Unglaube F, et al.
PLoS One
. 2019 Aug;
14(8):e0220899.
PMID: 31465469
Objective: To describe users' and therapists' opinions on multi-function myoelectric upper limb prostheses with conventional control and pattern recognition control. Design: Qualitative interview study. Settings: Two rehabilitation institutions in the...