Correction: Gait parameters are differently affected by concurrent smartphone-based activities with scaled levels of cognitive effort.

[This corrects the article DOI: 10.1371/journal.pone.

Gait parameters are differently affected by concurrent smartphone-based activities with scaled levels of cognitive effort.

The widespread and pervasive use of smartphones for sending messages, calling, and entertainment purposes, mainly among young adults, is often accompanied by the concurrent execution of other tasks. Recent studies have analyzed how texting, reading or calling while walking-in some specific conditions-might significantly influence gait parameters. The aim of this study is to examine the effect of different smartphone activities on walking, evaluating the variations of several gait parameters.

Pre-Processing Effect on the Accuracy of Event-Based Activity Segmentation and Classification through Inertial Sensors.

Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. For monitoring and fitness applications, it is crucial to develop methods able to segment each activity cycle, e.g.

Varying behavior of different window sizes on the classification of static and dynamic physical activities from a single accelerometer.

Accuracy of systems able to recognize in real time daily living activities heavily depends on the processing step for signal segmentation. So far, windowing approaches are used to segment data and the window size is usually chosen based on previous studies. However, literature is vague on the investigation of its effect on the obtained activity recognition accuracy, if both short and long duration activities are considered.

SVM versus MAP on accelerometer data to distinguish among locomotor activities executed at different speeds.

Two approaches to the classification of different locomotor activities performed at various speeds are here presented and evaluated: a maximum a posteriori (MAP) Bayes' classification scheme and a Support Vector Machine (SVM) are applied on a 2D projection of 16 features extracted from accelerometer data. The locomotor activities (level walking, stair climbing, and stair descending) were recorded by an inertial sensor placed on the shank (preferred leg), performed in a natural indoor-outdoor scenario by 10 healthy young adults (age 25-35 yrs.).

Inter-individual variability of forces and modular muscle coordination in cycling: a study on untrained subjects.

The aim of this study was to investigate the muscle coordination underlying pedaling in untrained subjects by using the muscle synergies paradigm, and to connect it with the inter-individual variability of EMG patterns and applied forces. Nine subjects performed a pedaling exercise on a cycle-simulator. Applied forces were recorded by means of instrumented pedals able to measure two force components.

Biologically inspired modelling for the control of upper limb movements: from concept studies to future applications.

Modelling is continuously being deployed to gain knowledge on the mechanisms of motor control. Computational models, simulating the behaviour of complex systems, have often been used in combination with soft computing strategies, thus shifting the rationale of modelling from the description of a behaviour to the understanding of the mechanisms behind it. In this context, computational models are preferred to deterministic schemes because they deal better with complex systems.

A neural tracking and motor control approach to improve rehabilitation of upper limb movements.

Background: Restoration of upper limb movements in subjects recovering from stroke is an essential keystone in rehabilitative practices. Rehabilitation of arm movements, in fact, is usually a far more difficult one as compared to that of lower extremities. For these reasons, researchers are developing new methods and technologies so that the rehabilitative process could be more accurate, rapid and easily accepted by the patient.

A biologically inspired neural network controller for ballistic arm movements.

Background: In humans, the implementation of multijoint tasks of the arm implies a highly complex integration of sensory information, sensorimotor transformations and motor planning. Computational models can be profitably used to better understand the mechanisms sub-serving motor control, thus providing useful perspectives and investigating different control hypotheses. To this purpose, the use of Artificial Neural Networks has been proposed to represent and interpret the movement of upper limb.