BACKGROUND: Approximately 5%-10% of elementary school children show delayed development of fine motor skills. To address these problems, detection is required. Current assessment tools are time-consuming, require a trained supervisor, and are not motivating for children. Sensor-augmented toys and machine learning have been presented as possible solutions to address this problem.OBJECTIVE: This study examines whether sensor-augmented toys can be used to assess children's fine motor skills. The objectives were to (1) predict the outcome of the fine motor skill part of the Movement Assessment Battery for Children Second Edition (fine MABC-2) and (2) study the influence of the classification model, game, type of data, and level of difficulty of the game on the prediction.METHODS: Children in elementary school (n=95, age 7.8 [SD 0.7] years) performed the fine MABC-2 and played 2 games with a sensor-augmented toy called "Futuro Cube." The game "roadrunner" focused on speed while the game "maze" focused on precision. Each game had several levels of difficulty. While playing, both sensor and game data were collected. Four supervised machine learning classifiers were trained with these data to predict the fine MABC-2 outcome: k-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), and support vector machine (SVM). First, we compared the performances of the games and classifiers. Subsequently, we compared the levels of difficulty and types of data for the classifier and game that performed best on accuracy and F1 score. For all statistical tests, we used α=.05.RESULTS: The highest achieved mean accuracy (0.76) was achieved with the DT classifier that was trained on both sensor and game data obtained from playing the easiest and the hardest level of the roadrunner game. Significant differences in performance were found in the accuracy scores between data obtained from the roadrunner and maze games (DT, P=.03; KNN, P=.01; LR, P=.02; SVM, P=.04). No significant differences in performance were found in the accuracy scores between the best performing classifier and the other 3 classifiers for both the roadrunner game (DT vs KNN, P=.42; DT vs LR, P=.35; DT vs SVM, P=.08) and the maze game (DT vs KNN, P=.15; DT vs LR, P=.62; DT vs SVM, P=.26). The accuracy of only the best performing level of difficulty (combination of the easiest and hardest level) achieved with the DT classifier trained with sensor and game data obtained from the roadrunner game was significantly better than the combination of the easiest and middle level (P=.046).CONCLUSIONS: The results of our study show that sensor-augmented toys can efficiently predict the fine MABC-2 scores for children in elementary school. Selecting the game type (focusing on speed or precision) and data type (sensor or game data) is more important for determining the performance than selecting the machine learning classifier or level of difficulty.
Movement is an essential part of our lives. Throughout our lifetime, we acquire many different motor skills that are necessary to take care of ourselves (e.g., eating, dressing), to work (e.g., typing, using tools, care for others) and to pursue our hobbies (e.g., running, dancing, painting). However, as a consequence of aging, trauma or chronic disease, motor skills may deteriorate or become “lost”. Learning, relearning, and improving motor skills may then be essential to maintain or regain independence. There are many different ways in which the process of learning a motor skill can be shaped in practice. The conceptual basis for this thesis was the broad distinction between implicit and explicit forms of motor learning. Physiotherapists and occupational therapists are specialized to provide therapy that is tailored to facilitate the process of motor learning of patients with a wide range of pathologies. In addition to motor impairments, patients suffering from neurological disorders often also experience problems with cognition and communication. These problems may hinder the process of learning at a didactic level, and make motor learning especially challenging for those with neurological disorders. This thesis focused on the theory and application of motor learning during rehabilitation of patients with neurological disorders. The overall aim of this thesis was to provide therapists in neurological rehabilitation with knowledge and tools to support the justified and tailored use of motor learning in daily clinical practice. The thesis is divided into two parts. The aim of the first part (Chapters 2‐5) was to develop a theoretical basis to apply motor learning in clinical practice, using the implicit‐explicit distinction as a conceptual basis. Results of this first part were used to develop a framework for the application of motor learning within neurological rehabilitation (Chapter 6). Afterwards, in the second part, strategies identified in first part were tested for feasibility and potential effects in people with stroke (Chapters 7 and 8). Chapters 5-8 are non-final versions of an article published in final form in: Chapter 5: Kleynen M, Moser A, Haarsma FA, Beurskens AJ, Braun SM. Physiotherapists use a great variety of motor learning options in neurological rehabilitation, from which they choose through an iterative process: a retrospective think-aloud study. Disabil Rehabil. 2017 Aug;39(17):1729-1737. doi: 10.1080/09638288.2016.1207111. Chapter 6: Kleynen M, Beurskens A, Olijve H, Kamphuis J, Braun S. Application of motor learning in neurorehabilitation: a framework for health-care professionals. Physiother Theory Pract. 2018 Jun 19:1-20. doi: 10.1080/09593985.2018.1483987 Chapter 7: Kleynen M, Wilson MR, Jie LJ, te Lintel Hekkert F, Goodwin VA, Braun SM. Exploring the utility of analogies in motor learning after stroke: a feasibility study. Int J Rehabil Res. 2014 Sep;37(3):277-80. doi: 10.1097/MRR.0000000000000058. Chapter 8: Kleynen M, Jie LJ, Theunissen K, Rasquin SM, Masters RS, Meijer K, Beurskens AJ, Braun SM. The immediate influence of implicit motor learning strategies on spatiotemporal gait parameters in stroke patients: a randomized within-subjects design. Clin Rehabil. 2019 Apr;33(4):619-630. doi: 10.1177/0269215518816359.
Motor activation is rarely integrated into the support of people with profound intellectual and multiple disabilities (PIMD), which might be the result of the limited evidence-based knowledge in this field. Practitioners have recently been developing several motor initiatives for people with PIMD, but it remains unclear about what core elements the motor initiatives actually consist of and towhat level of quality it is implemented in practice. This study aims to offer an overview and analysis of the content and quality of motor initiatives actually in use for people with PIMD. Motor initiatives were explored by asking practitioners to complete an online inventory form. Documents, expert knowledge, and observations were used to collect data about the characteristics of themotor initiatives. The quality of the motor initiatives which met our eligibility criteria, was analyzed on the basis of the level of evidencefor their effectiveness. The inventory yielded 118 motor initiatives of which 17 met the eligibility criteria. We identified four motor initiatives reflecting an approach to motorically activate people with PIMD within various activities, three including powerassisted exercises, three with aquatic exercises, two frameworks which integrated motor activities into their daily programs, two methods which included small-scale activities, two rhythmic movement therapies, and one program including gross motor activities. We found limited indications for descriptive evidence from 17 initiatives, limited or no indications for theoretical evidence from 12 and five initiatives respectively, and none of the initiatives provided a causal level of evidence for effectiveness. A wide variety of motor initiatives is used in current practice to activate persons with PIMD, although their effectiveness is actually unproven.Science and practice should cooperate to develop an evidence-based understanding to ensure more evidence-based support for themotor activation of people with PIMD in the future.
Paper sludge contains papermaking mineral additives and fibers, which could be reused or recycled, thus enhancing the circularity. One of the promising technologies is the fast pyrolysis of paper sludge, which is capable of recovering > 99 wt.% of the fine minerals in the paper sludge and also affording a bio-liquid. The fine minerals (e.g., ‘circular’ CaCO3) can be reused as filler in consumer products thereby reducing the required primary resources. However, the bio-liquid has a lower quality compared to fossil fuels, and only a limited application, e.g., for heat generation, has been applied. This could be significantly improved by catalytic upgrading of the fast pyrolysis vapor, known as an ex-situ catalytic pyrolysis approach. We have recently found that a high-quality bio-oil (mainly ‘bio-based’ paraffins and low-molecular-weight aromatics, carbon yield of 21%, and HHV of 41.1 MJ kg-1) was produced (Chem. Eng. J., 420 (2021), 129714). Nevertheless, catalyst deactivation occurred after a few hours’ of reaction. As such, catalyst stability and regenerability are of research interest and also of high relevance for industrial implementation. This project aims to study the potential of the add-on catalytic upgrading step to the industrial fast pyrolysis of paper sludge process. One important performance metric for sustainable catalysis in the industry is the level of catalyst consumption (kgcat tprod-1) for catalytic pyrolysis of paper sludge. Another important research topic is to establish the correlation between yield and selectivity of the bio-chemicals and the catalyst characteristics. For this, different types of catalysts (e.g., FCC-type E-Cat) will be tested and several reaction-regeneration cycles will be performed. These studies will determine under which conditions catalytic fast pyrolysis of paper sludge is technically and economically viable.
