Abstract
Reliable detection of gait events is important to ensure accurate assessment of gait. While it is usually performed resorting to force platforms, methods based uniquely on kinematic analysis have also been proposed. These methods place no restrictions on the number of steps that can be analysed, simplifying setup and complexity of assessments. They also replace the need of annotating events manually when force platforms are not available. Although few methods have been proposed in literature, validation studies are relatively scarce. In this study we present multiple methods for the detection of heel strike (HS) and toe off (TO) in normal walking, and validate the detection against annotated events using three different datasets. The best performing candidates are based on the evaluation of heel vertical velocity (for HS) and toe vertical acceleration (for TO), resulting in relative errors of -12.4 +/- 32.9 ms for HS and of -15.5 +/- 24.9 ms for TO. The method is compatible with barefoot and shod walking, constituting a convenient, fast and reliable alternative to automatic gait event detection using kinematic data.

Abstract
This study presents lower limb joint load comparison from subject specific musculoskeletal model simulation (MSK-MS) and direct measurements from instrumented implants on post-operative (PO) patients. A case study was considered for MSK-MS gait analysis of a 40-year-old healthy male with 70 kg and 1.86 m height. Reflective adhesive markers were applied on skin surface of selected anatomical points at right and left lower limbs. Orthostatic and dynamic acquisition on normal gait (NG), stiff-knee gait (SKG) and slow running (SR) was performed from ground reaction forces with two force plates at 2 kHz and trajectories of skin markers with eight-camera system at 100 Hz. Subject specific MSK-MS was performed using AnyGait and morphed Twente Lower Extremity Model (TLEM), matching the size and joint morphology of the stick-figure model. Over-determinate kinematic analysiswas performed, and motion equations solved with hard and soft constraints. Representative MSK-MS gait cycles were selected at NG, SKG and SR lower limb joint vertical force components at the hip, the knee, and the ankle normalized to body weight (JFz/BW). Internal joint direct measurements of four PO patients', 61-83 years, average weight 808 N and 1.71 m height, with telemetric Hip I (4-channel), Hip II (8-channel) and knee (9-channel) instrumented implants were selected from Orthoload database with comparable gait to NG, SKG and SR. Statistical measurements presented similar mean JFz/BW at right/left hip, knee, ankle MSK-MS and asymmetric peak values with dominant NG, SKG and SR different variances (p < 0.05). Direct JFz/BW measures contrasted NG with similar hip and knee mean and variance from SKG and SR with different mean and variance. Peak JFz/BW direct measurements presented higher hip and knee values on SR and NG than SKG, with higher values at the knee than the hip on NG and SKG, and the opposite on SR. Direct JFz/BW measurements presented at the hip and the knee lower values than their corresponding MSK-MS on NG, SKG and SR.

Abstract
Peripheral arterial disease (PAD) causes blockage of the arteries, altering the blood flow to the lower limbs. This blockage can cause the individual with PAD to feel severe pain in the lower limbs. The main contribution of this research is the discovery of a solution that allows the automatic detection of the onset of claudication based on data analysis from patients' smartphones. For the data-collection procedure, 40 patients were asked to walk with a smartphone on a thirty-meter path, back and forth, for six minutes. Each patient conducted the test twice on two different days. Several machine learning models were compared to detect the onset of claudication on two different datasets. The results suggest that we can identify the onset of claudication using inertial sensors with a best case accuracy of 92.25% for the Extreme Gradient Boosting model.

Abstract
Wearable/portable biometric/physiological monitoring devices are rapidly becoming a recognized alternative in medicine, rehabilitation and sports. Developments in sensors, energy harvesting, embedded technology, smart textile, to mention a few, are driving the field to more seamless and complex solutions, sometimes part of pervasive strategies for activity monitoring. Additionally, the number of sensors forming part of wearable solutions seems to be incrementing thanks to miniaturization and lowering components cost. Consequently medical and rehabilitation protocols and standards are undergoing the slow process required for adaptation to such emerging trends. This article presnts a simple modular, low-cost, wearable device originally intended for rehabilitation data gathering. Such device was based on commercially available components which can be assembled and managed by physicians, therapist and other healthcare personnel through a proposed platform. The objective is the familiarization and even active inclusion of healthcare personnel in the technological development process and, more importantly, the incorportation of electronic data acquisition in their procedures.

Abstract
BIOSWIM (Body Interface System based on Wearable Integration Monitoring) is a joint multidisciplinary effort involving a number of Portuguese R&D teams. It seeks a pervasive monitoring solution for physiological and biomechanical signals from a swimmer under normal training conditions, both in and out of the water. A wearable inertial monitoring unit (WIMU) was developed in order to serve as the biomechanical data processing unit of the system. The preliminary version of the WIMU has a commercially available microcontroller and transceiver set, as well as a tri-axial accelerometer and a bi-axial gyroscope serving as sensors. Testing in and out of the water has provided promising data and contributed to design modifications. These also took into account input from athletes, trainers, and physicians. Future work includes the integration of the WIMU within the complete BIOSWIM swimsuit system, complemented by truly integrated EMG and ECG textile sensors and a chemical monitoring unit. © Springer-Verlag Berlin Heidelberg 2013.

Abstract