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This paper presents a novel multimodal virtual rehabilitation environment. Its design and implementation are based on principles related to intrinsic motivation and game design. The system consists of visual, acoustic, and haptic modalities. Elements contributing to intrinsic motivation are carefully joined in the three modalities to increase patients' motivation during the long process of rehabilitation. The message in a bottle (MIB) virtual scenario is designed to allow interplay between motor and cognitive challenges in the exercising patient. The user first needs to perform a motor action to receive a cognitive challenge that is finally solved by a second motor action. Visual feedback provides the most relevant information related to the task. Acoustic feedback consists of environmental sounds, music, and spoken instructions or encouraging statements for the patient. The haptic modality generates tactile information related to the environment and provides various modes of assistance for the patient's arm movements. The MIB scenario was evaluated with 16 stroke patients, who rated it positively using the Intrinsic Motivation Inventory questionnaire. Additionally, the MIB scenario seems to elicit higher motivation than a simpler pick-and-place training task.

This paper presents a gait phase detection algorithm for providing feedback in walking with a robotic prosthesis. The algorithm utilizes the output signals of a wearable wireless sensory system incorporating sensorized shoe insoles and inertial measurement units attached to body segments. The principle of detecting transitions between gait phases is based on heuristic threshold rules, dividing a steady-state walking stride into four phases. For the evaluation of the algorithm, experiments with three amputees, walking with the robotic prosthesis and wearable sensors, were performed. Results show a high rate of successful detection for all four phases (the average success rate across all subjects >90%). A comparison of the proposed method to an off-line trained algorithm using hidden Markov models reveals a similar performance achieved without the need for learning dataset acquisition and previous model training.

Real-time recognition of locomotion-related activities is a fundamental skill that a controller of lower-limb wearable robots should possess. Subject-specific training and reliance on electromyographic interfaces are the main limitations of existing approaches. This study presents a novel methodology for realtime locomotion mode recognition of locomotion-related activities in lower-limb wearable robotics. A hybrid classifier can distinguish among seven locomotion-related activities. First, a timebased approach classifies between static and dynamical states based on gait kinematics data. Second, an event-based fuzzy logic method triggered by foot pressure sensors operates in a subjectindependent fashion on a minimal set of relevant biomechanical features to classify among dynamical modes. The locomotion mode recognition algorithm is implemented on the controller of a portable powered orthosis for hip assistance. An experimental protocol is designed to evaluate the controller performance in an out-of-lab scenario without the need for a subject-specific training. Experiments are conducted on six healthy volunteers performing locomotion-related activities at slow, normal, and fast speeds under the zero-torque and assistive mode of the orthosis. The overall accuracy rate of the controller is 99.4% over more than 10,000 steps, including seamless transitions between different modes. The experimental results show a successful subject-independent performance of the controller for wearable robots assisting locomotion-related activities.