Exercise and Proximal Femur Bone Strength to Reduce Fall-Induced Hip Fracture

Bone mass and structure, constituting its strength, adapt to prevalent mechanical environment. Physical activity and exercise provide natural ways to apply the mechanical loading to bone. Finding effective osteogenic exercise types to improve proximal femur bone strength is of great importance to reduce hip fracture incidence and consequent substantial socioeconomic burden. Importantly, almost all hip fractures are caused by falls. Therefore, the primary objective of the present doctoral research was to find such effective exercise types by exploring the effect of long-term specific exercise loading on proximal femur bone strength in the fall situation using a finite element (FE) method. The secondary objective was to analyze 3D morphological adaptation of proximal femur cortical bone to the specific exercise loading. The results from this secondary objective were anticipated to help understanding the findings pertinent to the primary objective. To achieve these objectives, proximal femur MRI data were obtained from 91 young adult female athletes (aged 24.7 ± 6.1 years, > 8 years competing career) and 20 nonathletic but physically active controls (aged 23.7 ± 3.8 years). The athletes were classified into five distinct exercise loading groups based on the typical loading patterns of their sports: high-impact (H-I: triple- and high-jumpers), odd-impact (O-I: soccer/football and squash players), high-magnitude (H-M: powerlifters), repetitive-impact (R-I: endurance runners), and repetitive non-impact (R-NI: swimmers). Based on their MRI data, proximal femur FE models were first created in a single fall configuration (direction) to compare 1) cortical stresses in eight anatomical octants of femoral neck cross-sections in the proximal, middle, and distal femoral neck regions and 2) fracture behavior (load, location, and mode) between each exercise loading and control groups. The athletic bones are adapted to the long- term specific exercise loading characterized by not only the loading magnitude, rate, and frequency but also direction. Given this, the study was extended to simulate the FE models in multiple fall directions to examine whether potentially identified higher proximal femur bone strength to reduce fall-induced hip fracture risk, attributed to the long-term specific exercise loading, depends on the direction of the fall onto the greater trochanter or hip. For the secondary objective, a new computational anatomy method called Ricci-flow conformal mapping (RCM) was implemented to obtain 3D distribution of the cortical thickness within the proximal femur and to perform its spatial between-group statistical comparisons. Key results from the present research demonstrated that young adult females with the exercise loading history of high ground impacts (H-I), ground impacts from unusual/odd directions (O-I), or a great number of repetitive ground impacts (R-I) had 10-22%, 12-16%, and 14-23% lower fall-induced cortical stress at the fracture-prone superolateral femoral neck and 11-17%, 10-11%, and 22-28% higher fracture loads (higher proximal femur bone strength) in the fall situations compared to the controls, respectively. These results indicate that the long-term H-I, O-I, and R-I exercise loadings may reduce the fall-induced hip fracture risk. Furthermore, the present results showed that the higher proximal femur bone strength to reduce hip fracture risk in athletes engaged in the high-impact or repetitive-impact sports are robust and independent of the direction of fall. In contrast, the higher strength attributed to the odd-impact exercise loading appears more modest and specific to the fall direction. The analysis of the minimum fall strength spanning the multiple fall directions also supported the higher proximal femur bone strength in the athletes engaged in these impact exercises. In concordance with the literature, the present results also confirmed in these young adult females that 1) the fall-induced hip fracture most likely initiates from the superolateral femoral neck’s cortical bone, particularly at its posterior aspect (superoposterior cortex) in the distal femoral neck region, and 2) the most dangerous fracture-causing fall direction is the one where the impact is imposed to the posterolateral aspect of the greater trochanter. It would be ideal if impact exercise loading could induce beneficial cortical bone adaptation in the fracture-prone posterior aspect of superolateral femoral neck cortex. However, such apparently beneficial cortical adaptation was not observed in any of the impact or nonimpact exercise loading types examined in the present research based on the supplementary RCM-based 3D morphological analyses of proximal femur cortical bone. This analysis importantly showed that the higher proximal femur bone strengths to reduce fall-induced hip fracture risk in athletes engaged in the high- or odd-impact exercise types are likely due to thicker cortical layers in other femoral neck regions including the inferior, posterior, and/or superior-to-superoanterior regions. Interestingly, the higher proximal femur strength in the athletes with the repetitive-impact exercise loading was not supported by such cortical adaptation. This suggests that other structural/geometrical adaptation contributes to their higher strength. This calls for further studies to elucidate the source of the higher proximal femur bone strength in this type of athletes. In contrast to the impact exercise loading histories, the exercise loading history of the high-magnitude (e.g., powerlifting) or repetitive, non-impact (e.g., swimming) was not associated with higher proximal femur bone strength to reduce fall-induced hip fracture risk. This most likely reflects the lack of any beneficial structural adaptations of cortical bone around the femoral neck in the athletes with these exercise loading histories. Considering the loading characteristics of the exercise types examined in the present doctoral research, the moderate-to-high loading magnitude alone appears insufficient but needs to be generated at the high loading rate and/or frequency to induce the beneficial adaptation in the proximal femur cortical bone. Therefore, in addition to aforementioned three impact exercise loading types, other exercise or sport types satisfying this condition may also be effective to increase or maintain the proximal femur bone strength to reduce fall- induced hip fracture risk. As a clinical prospect, the present findings highlight the importance of impact exercise in combating fall-induced hip fracture. Compared to the high-impact loading exercises (e.g., triple/long and high jumping exercise), the odd-impact [ball or invasion games (e.g., football/soccer, tennis)] and/or repetitive-impact loading exercises (e.g., endurance running, jogging, and perhaps vigorous walking) likely provide a safer and more feasible choice for the populations covering the sedentary adults to old people. This is due to the relatively more moderate ground impact involved in the odd- and repetitive-impact loading exercises than in the high-impact exercises. For young, physically active, and/or fit people, the above-mentioned or similar jumping exercises and any other exercise types consisting of the high ground impact (e.g., volleyball, basketball, gymnastics) can also be incorporated into their habitual exercise routines. Lastly, the present results were observed in the young adult females who had engaged in sport-specific training from their childhood/adolescence to early adulthood. Therefore, this calls for the prospective and/or retrospective observational studies to investigate whether the higher proximal femur bone strength to reduce fall-induced hip fracture risk obtained from the long-term specific impact exercise loading during these early phases of life can sustain into the later stages, especially after age of 65 years when the hip fracture is generally more common

Image-set, Temporal and Spatiotemporal Representations of Videos for Recognizing, Localizing and Quantifying Actions

This dissertation addresses the problem of learning video representations, which is defined here as transforming the video so that its essential structure is made more visible or accessible for action recognition and quantification. In the literature, a video can be represented by a set of images, by modeling motion or temporal dynamics, and by a 3D graph with pixels as nodes. This dissertation contributes in proposing a set of models to localize, track, segment, recognize and assess actions such as (1) image-set models via aggregating subset features given by regularizing normalized CNNs, (2) image-set models via inter-frame principal recovery and sparsely coding residual actions, (3) temporally local models with spatially global motion estimated by robust feature matching and local motion estimated by action detection with motion model added, (4) spatiotemporal models 3D graph and 3D CNN to model time as a space dimension, (5) supervised hashing by jointly learning embedding and quantization, respectively. State-of-the-art performances are achieved for tasks such as quantifying facial pain and human diving. Primary conclusions of this dissertation are categorized as follows: (i) Image set can capture facial actions that are about collective representation; (ii) Sparse and low-rank representations can have the expression, identity and pose cues untangled and can be learned via an image-set model and also a linear model; (iii) Norm is related with recognizability; similarity metrics and loss functions matter; (v) Combining the MIL based boosting tracker with the Particle Filter motion model induces a good trade-off between the appearance similarity and motion consistence; (iv) Segmenting object locally makes it amenable to assign shape priors; it is feasible to learn knowledge such as shape priors online from Web data with weak supervision; (v) It works locally in both space and time to represent videos as 3D graphs; 3D CNNs work effectively when inputted with temporally meaningful clips; (vi) the rich labeled images or videos help to learn better hash functions after learning binary embedded codes than the random projections. In addition, models proposed for videos can be adapted to other sequential images such as volumetric medical images which are not included in this dissertation