@article{45824,
abstract = {{As cognitive function is critical for muscle coordination, cognitive training may also improve neuromuscular control strategy and knee function following an anterior cruciate ligament reconstruction (ACLR). The purpose of this case-control study was to examine the effects of cognitive training on joint stiffness regulation in response to negative visual stimuli and knee function following ACLR. A total of 20 ACLR patients and 20 healthy controls received four weeks of online cognitive training. Executive function, joint stiffness in response to emotionally evocative visual stimuli (neutral, fearful, knee injury related), and knee function outcomes before and after the intervention were compared. Both groups improved executive function following the intervention (p = 0.005). The ACLR group had greater mid-range stiffness in response to fearful (p = 0.024) and injury-related pictures (p = 0.017) than neutral contents before the intervention, while no post-intervention stiffness differences were observed among picture types. The ACLR group showed better single-legged hop for distance after cognitive training (p = 0.047), while the healthy group demonstrated no improvement. Cognitive training enhanced executive function, which may reduce joint stiffness dysregulation in response to emotionally arousing images and improve knee function in ACLR patients, presumably by facilitating neural processing necessary for neuromuscular control.}},
author = {{An, Yong Woo and Kim, Kyung-Min and DiTrani Lobacz, Andrea and Baumeister, Jochen and Higginson, Jill S. and Rosen, Jeffrey and Swanik, Charles Buz}},
issn = {{2227-9032}},
journal = {{Healthcare}},
keywords = {{Health Information Management, Health Informatics, Health Policy, Leadership and Management}},
number = {{13}},
publisher = {{MDPI AG}},
title = {{{Cognitive Training Improves Joint Stiffness Regulation and Function in ACLR Patients Compared to Healthy Controls}}},
doi = {{10.3390/healthcare11131875}},
volume = {{11}},
year = {{2023}},
}
@article{30906,
abstract = {{Abstract
Background
Hand amputation can have a truly debilitating impact on the life of the affected person. A multifunctional myoelectric prosthesis controlled using pattern classification can be used to restore some of the lost motor abilities. However, learning to control an advanced prosthesis can be a challenging task, but virtual and augmented reality (AR) provide means to create an engaging and motivating training.
Methods
In this study, we present a novel training framework that integrates virtual elements within a real scene (AR) while allowing the view from the first-person perspective. The framework was evaluated in 13 able-bodied subjects and a limb-deficient person divided into intervention (IG) and control (CG) groups. The IG received training by performing simulated clothespin task and both groups conducted a pre- and posttest with a real prosthesis. When training with the AR, the subjects received visual feedback on the generated grasping force. The main outcome measure was the number of pins that were successfully transferred within 20 min (task duration), while the number of dropped and broken pins were also registered. The participants were asked to score the difficulty of the real task (posttest), fun-factor and motivation, as well as the utility of the feedback.
Results
The performance (median/interquartile range) consistently increased during the training sessions (4/3 to 22/4). While the results were similar for the two groups in the pretest, the performance improved in the posttest only in IG. In addition, the subjects in IG transferred significantly more pins (28/10.5 versus 14.5/11), and dropped (1/2.5 versus 3.5/2) and broke (5/3.8 versus 14.5/9) significantly fewer pins in the posttest compared to CG. The participants in IG assigned (mean ± std) significantly lower scores to the difficulty compared to CG (5.2 ± 1.9 versus 7.1 ± 0.9), and they highly rated the fun factor (8.7 ± 1.3) and usefulness of feedback (8.5 ± 1.7).
Conclusion
The results demonstrated that the proposed AR system allows for the transfer of skills from the simulated to the real task while providing a positive user experience. The present study demonstrates the effectiveness and flexibility of the proposed AR framework. Importantly, the developed system is open source and available for download and further development.
}},
author = {{Boschmann, Alexander and Neuhaus, Dorothee and Vogt, Sarah and Kaltschmidt, Christian and Platzner, Marco and Dosen, Strahinja}},
issn = {{1743-0003}},
journal = {{Journal of NeuroEngineering and Rehabilitation}},
keywords = {{Health Informatics, Rehabilitation}},
number = {{1}},
publisher = {{Springer Science and Business Media LLC}},
title = {{{Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis}}},
doi = {{10.1186/s12984-021-00822-6}},
volume = {{18}},
year = {{2021}},
}
@article{37154,
abstract = {{ The application of artificial intelligence (AI) not only yields in advantages for healthcare but raises several ethical questions. Extant research on ethical considerations of AI in digital health is quite sparse and a holistic overview is lacking. A systematic literature review searching across 853 peer-reviewed journals and conferences yielded in 50 relevant articles categorized in five major ethical principles: beneficence, non-maleficence, autonomy, justice, and explicability. The ethical landscape of AI in digital health is portrayed including a snapshot guiding future development. The status quo highlights potential areas with little empirical but required research. Less explored areas with remaining ethical questions are validated and guide scholars’ efforts by outlining an overview of addressed ethical principles and intensity of studies including correlations. Practitioners understand novel questions AI raises eventually leading to properly regulated implementations and further comprehend that society is on its way from supporting technologies to autonomous decision-making systems. }},
author = {{Möllmann, Nicholas RJ and Mirbabaie, Milad and Stieglitz, Stefan}},
issn = {{1460-4582}},
journal = {{Health Informatics Journal}},
keywords = {{Health Informatics}},
number = {{4}},
publisher = {{SAGE Publications}},
title = {{{Is it alright to use artificial intelligence in digital health? A systematic literature review on ethical considerations}}},
doi = {{10.1177/14604582211052391}},
volume = {{27}},
year = {{2021}},
}