Physical activity and osseointegration: "step forward" to improve the quality of life of a trans-femoral amputee person

Lower limb amputation is an event that inevitably changes the lifestyle of the person with a significant impact on quality of life. The socket-type prosthesis entails that the residual limb is in direct contact with the socket which often implies numerous disadvantages. Osseointegrated prosthesis is a solution that avoids skin problems because not include the presence of the socket. In this type of prosthesis, a stem is surgically inserted inside the medullary canal and connected with the external prosthetic limb. Therefore, this thesis aims to highlight and explore the main strengths and problems of osseointegrated prostheses and to examine the role of physical activity, with attention to functional capacity and bone quality. The objectives of the thesis will be developed through 5 studies: (I) A gait analysis of a 44 years-old male patient who underwent surgery for the implantation of an osseointegrated prosthesis; (II) A systematic review to investigate the state of stump bone quality in patients with limb amputations; (III) A systematic review of the technologies involved in such devices has been carried out to identify the most fruitful ones in improving bone quality; (IV) A systematic review investigating the topic of physical activity and bone turnover biomarkers; (V) A systematic review to investigate the effects of physical activity interventions combined with drug treatments on bone biomarkers in people with osteopenia and osteoporosis. The integrated prosthesis is a good solution for people with lower limb amputation who cannot use their traditional socket-type prosthesis. Although many objectives have already been achieved, there are still many aspects that we can improve. These include the creation of a multidisciplinary path that support patients along their path, with particular attention to the pre-surgery and the post-rehabilitation phase that is still lacking even if of fundamental impact in determining the quality of life.

Deploying and processing neural representations of signals

Neural representations (NR) have emerged in the last few years as a powerful tool to represent signals from several domains, such as images, 3D shapes, or audio. Indeed, deep neural networks have been shown capable of approximating continuous functions that describe a given signal with theoretical infinite resolution. This finding allows obtaining representations whose memory footprint is fixed and decoupled from the resolution at which the underlying signal can be sampled, something that is not possible with traditional discrete representations, e.g., grids of pixels for images or voxels for 3D shapes. During the last two years, many techniques have been proposed to improve the capability of NR to approximate high-frequency details and to make the optimization procedures required to obtain NR less demanding both in terms of time and data requirements, motivating many researchers to deploy NR as the main form of data representation for complex pipelines. Following this line of research, we first show that NR can approximate precisely Unsigned Distance Functions, providing an effective way to represent garments that feature open 3D surfaces and unknown topology. Then, we present a pipeline to obtain in a few minutes a compact Neural Twin® for a given object, by exploiting the recent advances in modeling neural radiance fields. Furthermore, we move a step in the direction of adopting NR as a standalone representation, by considering the possibility of performing downstream tasks by processing directly the NR weights. We first show that deep neural networks can be compressed into compact latent codes. Then, we show how this technique can be exploited to perform deep learning on implicit neural representations (INR) of 3D shapes, by only looking at the weights of the networks.