Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel wavelet-based approach to fall detection, focusing on the impact phase and using a dataset of real-world falls. Since recorded falls result in a non-stationary signal, a wavelet transform was chosen to examine fall patterns. The idea is to consider the average fall pattern as the “prototype fall”.In order to detect falls, every acceleration signal can be compared to this prototype through wavelet analysis. The similarity of the recorded signal with the prototype fall is a feature that can be used in order to determine the difference between falls and daily activities. The discriminative ability of this feature is evaluated on real-world data. It outperforms other features that are commonly used in fall detection studies, with an Area Under the Curve of 0.918. This result suggests that the proposed wavelet-based feature is promising and future studies could use this feature (in combination with others considering different fall phases) in order to improve the performance of fall detection algorithms.

Inertial sensors are becoming widely used for the assessment of human movement in both clinical and research applications, thanks to their usability out of the laboratory. This work aims to propose a method for calibrating anatomical landmark position in the wearable sensor reference frame with an ease to use, portable and low cost device. An off-the-shelf camera, a stick and a pattern, attached to the inertial sensor, compose the device. The proposed technique is referred to as video Calibrated Anatomical System Technique (vCAST). The absolute orientation of a synthetic femur was tracked both using the vCAST together with an inertial sensor and using stereo-photogrammetry as reference. Anatomical landmark calibration showed mean absolute error of 0.6±0.5 mm: these errors are smaller than those affecting the in-vivo identification of anatomical landmarks. The roll, pitch and yaw anatomical frame orientations showed root mean square errors close to the accuracy limit of the wearable sensor used (1°), highlighting the reliability of the proposed technique. In conclusion, the present paper proposes and preliminarily verifies the performance of a method (vCAST) for calibrating anatomical landmark position in the wearable sensor reference frame: the technique is low time consuming, highly portable, easy to implement and usable outside laboratory.

The EEG rhythmic activities of the somato-sensory cortex reveal event-related desynchronization (ERD) or event-related synchronization (ERS) in beta band (14-30 Hz) as subjects perform certain tasks or react to specific stimuli. Data reported for imagination of movement support the hypothesis that activation of one sensorimotor area can be accompanied by deactivation of the other. In order to improve our understanding of beta ERD/ERS generation, two neural mass models of a cortical column taken from Wendling et al (2002) were interconnected to simulate the transmission of information from one cortex to the other. The results show that the excitation of one cortex leads to inhibition of the other and vice versa, enforcing the Theory of Inhibition. This behaviour strongly depends on the initial working point of the neural populations (between the linear and the upper saturation region of a sigmoidal function) and on how the cortical activation or deactivation can move the working point in the upper saturation region (ERD) or in the linear region (ERS) respectively.

This paper reports the architecture of a low-cost smart crutches system for mobile health applications. The prototype is based on a set of sensorized crutches connected to a custom Android application. Crutches were instrumented with a 6-axis inertial measurement unit, a uniaxial load cell, WiFi connectivity, and a microcontroller for data collection and processing. Crutch orientation and applied force were calibrated with a motion capture system and a force platform. Data are processed and visualized in real-time on the Android smartphone and are stored on the local memory for further offline analysis. The prototype’s architecture is reported along with the post-calibration accuracy for estimating crutch orientation (5° RMSE in dynamic conditions) and applied force (10 N RMSE). The system is a mobile-health platform enabling the design and development of real-time biofeedback applications and continuity of care scenarios, such as telemonitoring and telerehabilitation.

Canonical Correlation Analysis (CCA) is an increasingly used approach in the field of Steady-State Visually Evoked Potential (SSVEP) recognition. The efficacy of the method has been widely proven, and several variations have been proposed. However, most CCA variations tend to complicate the method, usually requiring additional user training or increasing computational load. Taking simple procedures and low computational costs may be, however, a relevant aspect, especially in view of low-cost and high-portability devices. In addition, it would be desirable that the proposed variations are as general and modular as possible to facilitate the translation of results to different algorithms and setups. In this work, we evaluated the impact of two simple, modular variations of the classical CCA method. The variations involved (i) the number of canonical correlations used for classification and (ii) the inclusion of a prefiltering step by means of sinc-windowing. We tested ten volunteers in a 4-class SSVEP setup. Both variations significantly improved classification accuracy when they were used separately or in conjunction and led to accuracy increments up to 7-8% on average and peak of 25–30%. Additionally, variations had no (variation (i)) or minimal (variation (ii)) impact on the number of algorithm steps required for each classification. Given the modular nature of the proposed variations and their positive impact on classification accuracy, they might be easily included in the design of CCA-based algorithms that are even different from ours.