Gait analysis was performed on 20 patients with unilateral hip prosthesis (3, 6 and 12 months post-operatively) and 20 controls to investigate their gait characteristics and muscle activation patterns. One year after the intervention, patients still walked with a higher percentage of "atypical" cycles, a prolonged heel contact, a shortened flat foot contact, a reduced hip dynamic range of motion and abnormal timing in the muscle activation patterns of tibialis anterior, gastrocnemius lateralis, biceps femoris and gluteus medius, with respect to the control group. Although the gait velocity and the knee range of motion improved from 3 to 6 months post-surgery, the above mentioned parameters did not improve from 6 to 12 months. THA patients failed to obtain normal gait one year after surgery.

As sensor-rich mobile devices became a commodity, more opportunities appeared for the creation of location-aware services. While GPS is a well established solution for outdoor localization, there is still no standard solution for localization indoors. This paper presents a novel accurate indoor positioning mechanism that is meant to run in common smartphones to be a readily and widely available solution. The system is based on multiple gait-model based filtering techniques for accurate movement quantification in combination with an advanced fused positioning mechanism that leverages sequences of opportunistic observations towards an accurate localization process. Magnetic field fluctuations, Wi-Fi readings and movement data are incrementally matched with a feature spot map containing multi-dimensional spatially-related features that characterize the building. A novel and convenient way of mapping the architectural and environmental properties of buildings is also introduced, which avoids the burden normally associated with the process. The system has been evaluated by multiple users in open and crowded spaces where overall median localization errors between 1.11 m and 1.68 m were obtained. While the reported errors are already satisfactory in the context of indoor localization, improvements may be readily achieved through the inclusion of additional reference features. High accuracy performance coupled with an opportunistic and infrastructure-free approach creates a very desirable solution for the indoor localization market doge

Monitoring physical activity and energy expenditure is important for maintaining adequate activity levels with an impact in health and well-being. This paper presents a smartphone based method for classification of inactive postures and physical activities including the calculation of energy expenditure. The implemented solution considers two different positions for the smartphone, the user's pocket or belt. The signal from the accelerometer embedded in the smartphone is used to classify the activities resorting to a decision tree classifier. The average accuracy of the classification task for all activities is 99.5% for the pocket usage and 99.4% when the phone is used in the belt. Using the output of the activity classifier we also compute an estimation of the energy expended by the user. The proposed solution is a trustworthy smartphone based activity monitor, classifying the activities of daily living throughout the entire day and allowing to assess the associated energy expenditure without causing any change in user's routines

Quantifying the energy expended during physical activity is an important metric to evaluate the quality and progress of individual training. There are several methods to estimate the energy expenditure using accelerometers, the most common are based on calculating counts per minute from the accelerometer signal to determinate the activity intensity in terms of metabolic equivalents (METs). This paper compares three methods to estimate the energy expenditure, the first has been proposed in a previous study and the last two are based on linear regressions derived from the data collected, one using speed, and the other using the feature root mean square (fRMS) of the magnitude of the accelerometer signal. These models were compared with indirect calorimetry outputs of energy expenditure during an incremental speed treadmill protocol. No statistically significant differences (p>0.05) were found between the indirect calorimetry and the model derived using the RMS feature, obtaining a normalized error of 20% for the METs estimation. In conclusion, this was found to be the most suitable method to estimate the energy expenditure from accelerometer data collected using a smartphone placed in the belt

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