## 1 Introduction  
The number of people with impaired mobility needing the assisting devices is rapidly increasing. Among the various orthopaedic and prosthetic solutions, wearable robotic devices can provide functional benefits in terms of therapy and daily living [1]. In addition, the robotic devices can improve the physical capabilities of healthy people by enhancing their performance and extending the time of use in industrial and military environments. These devices have made considerable progress in the past decade [2]. However, the main challenges remain low weight, actuators, human-exoskeleton interface, safety, energy efficiency, and lower cost [3]. Wearable sensory technologies have the ability to provide real-world motion-analysis information, which allows mobility limitations such as reduced walking speed/endurance and reduced physical activity to be assessed and addressed in different environments [4, 5, 6, 7]. Relevant research has been conducted mainly in laboratory or hospital settings over the past decade [8]. Typically, integrated sensors for monitoring a user's status are attached directly to the wearable robot or near the user's interaction points. In a local assessment, the sensors worn by the user promise a more accurate assessment of the current motion state and richer feedback information. This is the so-called Whole-Body Awareness (WBA) [9, 10]. In this way, the detection of the user's intended movement can be of higher quality. Within FP7-ICT-CYBERLEG we introduced WBA Control (WBAC) [9]: the control of actionsthat follow the user's state, using wearable sensors that monitor the user's kinetic and kinematic movements. The developed WSA network consists of non-invasive wearable insole sensors originating from the Scuola Superiore Sant'Anna in Pisa and a set of up to seven inertial measurement units (IMUs) from the University of Ljubljana. The IMU sensors measure the vectors of acceleration, angular velocity and magnetic field, and are attached to the user's lower limbs to assess the orientation of each unit [11; 12]. WSA is part of the real-time WBAC system that ideally provides effortless control over an active transfemoral prosthesis (ATP). Within the CL ++ H2020 project a new ATP was developed by the Vrije Universiteit Brussel [13] based on the previous version. WBAC approach is based on characteristic events detection in motion. In walking, typical phases are double support, which is characterized by the heel-strike and toe-off events, and single support, characterized by pattern of foot loading [14; 15]. Similarly, in stair raising, stair descending, stand-to-sit and sit-to-stand transfer the phases of stance, swing, standing and sitting are characterized by particular patterns of feet loading and leg segments motion [16]. The transitions between the different states are characterized by conditions, which in most cases are a combination of kinematic and/or static variables, and can be detected by different event-recognition techniques. Functional solutions from the literature have used input signals including foot-contact events [17; 18; 19; 20; 21; 22] as well as knee and segment angle [23; 24; 21], inter-joint angle [25; 26; 27; 28], linear segment acceleration and rotational velocity [29; 30], gait events and spatial parameters. Most implemented algorithms operate offline, with real-time implementations possible in cases where the minimum/maximum value of an observed variable is not sought. Fast events were localized in the time or frequency domains, eventually leading to a simple or complex decision tree described by decision rules. Wiring of a set of sensors attached to different positions at human body make usage of wearable devices difficult and not practical. Implementation of wireless data transfer and battery power supply of each sensory unit eases the donning and doffing and with this enables the every-day usage [31]. Portable wireless systems are commonly referred to as Wireless Body Area Networks (WBAN).