Through the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older... Show moreThrough the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older people and linking these to personal health gains. To be able to measure PAEE in a health care perspective, methods from wearable accelerometers have been developed, however, mainly targeted towards younger people. Since elderly subjects differ in energy requirements and range of physical activities, the current models may not be suitable for estimating PAEE among the elderly. Furthermore, currently available methods seem to be either simple but non-generalizable or require elaborate (manual) feature construction steps. Because past activities influence present PAEE, we propose a modeling approach known for its ability to model sequential data, the recurrent neural network (RNN). To train the RNN for an elderly population, we used the growing old together validation (GOTOV) dataset with 34 healthy participants of 60 years and older (mean 65 years old), performing 16 different activities. We used accelerometers placed on wrist and ankle, and measurements of energy counts by means of indirect calorimetry. After optimization, we propose an architecture consisting of an RNN with 3 GRU layers and a feedforward network combining both accelerometer and participant-level data. Our efforts included switching mean to standard deviation for down-sampling the input data and combining temporal and static data (person-specific details such as age, weight, BMI). The resulting architecture produces accurate PAEE estimations while decreasing training input and time by a factor of 10. Subsequently, compared to the state-of-the-art, it is capable to integrate longer activity data which lead to more accurate estimations of low intensity activities EE. It can thus be employed to investigate associations of PAEE with vitality parameters of older people related to metabolic and cognitive health and mental well-being. Show less
Through the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older... Show moreThrough the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older people and linking these to personal health gains. To be able to measure PAEE in a health care perspective, methods from wearable accelerometers have been developed, however, mainly targeted towards younger people. Since elderly subjects differ in energy requirements and range of physical activities, the current models may not be suitable for estimating PAEE among the elderly. Furthermore, currently available methods seem to be either simple but non-generalizable or require elaborate (manual) feature construction steps. Because past activities influence present PAEE, we propose a modeling approach known for its ability to model sequential data, the recurrent neural network (RNN). To train the RNN for an elderly population, we used the growing old together validation (GOTOV) dataset with 34 healthy participants of 60 years and older (mean 65 years old), performing 16 different activities. We used accelerometers placed on wrist and ankle, and measurements of energy counts by means of indirect calorimetry. After optimization, we propose an architecture consisting of an RNN with 3 GRU layers and a feedforward network combining both accelerometer and participant-level data. Our efforts included switching mean to standard deviation for down-sampling the input data and combining temporal and static data (person-specific details such as age, weight, BMI). The resulting architecture produces accurate PAEE estimations while decreasing training input and time by a factor of 10. Subsequently, compared to the state-of-the-art, it is capable to integrate longer activity data which lead to more accurate estimations of low intensity activities EE. It can thus be employed to investigate associations of PAEE with vitality parameters of older people related to metabolic and cognitive health and mental well-being. Show less
Despite modern advancements, total knee arthroplasty (TKA) is still far from perfection. With its dissatisfaction, which is 20-25%, the concept of TKA needs to be improved. TKA satisfaction is... Show moreDespite modern advancements, total knee arthroplasty (TKA) is still far from perfection. With its dissatisfaction, which is 20-25%, the concept of TKA needs to be improved. TKA satisfaction is associated with patient factors, surgical factors, and postoperative complications. Patient factors have an important role in TKA satisfaction, and one of that is to obtain good implant-bone fit are crucial in patient factors. It depends on patient’s ethnicities, socio-economic, and cultural. Therefore, special consideration is needed in Asian patients. Most TKA system, which is based on North American and European patients, could not be matched with Asian patients. Smaller sizes should be available to have a good implant-bone fit. But even with the TKA systems that were available, Asian patients had more significant improvement in range of motion compared to North American patients. It might be due to preoperative conditions and surgical factors. In the surgical factors, patellar denervation and accelerometer-based navigation were introduced. Patellar denervation failed to decrease AKP, while accelerometer-based navigation was also unable to show its superiority in improving functional outcomes. Therefore, the surgeon should decide at what point in the evolution of this emerging technology that the potential benefits of computer-assisted methods justify the costs and potential risks in an individual practice. In postoperative complications, VTE is one of the major complications. The racial may influence VTE risk. Hence, a different approach might be needed in VTE prevention for Asian patients. In the end, TKA remains a compromise to nature. Show less
