A genetic algorithm-based method to modulate the difficulty of serious games along consecutive robot-assisted therapy sessions.

BACKGROUND AND OBJECTIVE: One of the biggest challenges during neurorehabilitation therapies is finding an appropriate level of therapy intensity for each patient to ensure the recovery of movement of the affected limbs while maintaining motivation. Different studies have proposed adapting the difficulty of exercises based on psychophysiological state, based on success rate, or by modeling the user's skills. However, all studies propose solutions for a single session, requiring a calibration process before using it in each session. We propose a dynamic adaptation method that can be used during different rehabilitation sessions, without the need for recalibration between sessions. METHODS: The adaptation architecture is based on a genetic algorithm that aims to maintain a certain score level and to motivate the user to move. The method has been evaluated with two serious games for five sessions using a rehabilitation robot. A common initial evaluation was made for all the users involved in the study, and the game parameters that best suited each user from the previous session were introduced as the starting point of the next session. In addition, the desired score rate was lowered between sessions to increase the difficulty level. The psychophysiological state of the users was measured based on the Self-Assessment Manikin test, as well as different cardiorespiratory and galvanic skin response signals were analyzed. RESULTS: The adaptation architecture proposed can find those game parameters that maximize the user movement for both games. In one of the games, the score rate set for each session is followed with high fidelity. The degree of personalization in the games increases between sessions as the dispersion of the game parameters grows. The Self-Assessment Manikin test and the physiological signals results would indicate that the psychophysiological state remains equal between sessions despite an increase in game difficulty. CONCLUSIONS: The genetic algorithm-based game adaptation has proven efficacy in maximizing the therapy performance through the sessions without needing recalibration. It also can be concluded that the design of the game influences the adaptation performance. Additionally, adaptive game design facilitated by our method does not significantly impact players' emotional or physiological states.

Influence of Robotic Therapy on Severe Stroke Patients.

Robotic rehabilitation has emerged as a promising approach to enhance motor recovery after stroke, but there is limited knowledge about its efficacy in individuals who have experienced severe stroke. The study presented in this paper aims to analyze the effect of robotic therapy on the recovery of patients with severe stroke when combined with conventional rehabilitation therapies, and we want to observe whether there is a relationship between the clinical assessment provided by the therapist and the data recorded by the robotic device. Participants were divided into an experimental group and a control group, both receiving 15 sessions of conventional therapy in three consecutive weeks, but the experimental group underwent three out of five sessions per week with a robotic device. Both groups were evaluated using clinical scales, and in addition the experimental group was evaluated using an assessment game incorporated in the robotic device that provides session data such as the level of assistance needed by each user to complete the activity, or the score obtained in the game. These preliminary results showed that patients who received robot-assisted therapy had better motor function recovery compared to those who only received conventional therapy. In addition, it is also observed that the robot assistance needed by patients in the experimental group decreased as the sessions progressed, suggesting that robot-assisted therapy could be an effective tool for severe stroke patients.

Estimating vertical ground reaction forces during gait from lower limb kinematics and vertical acceleration using wearable inertial sensors.

One of the most important forces generated during gait is the vertical ground reaction force (vGRF). This force can be measured using force plates, but these can limit the scope of gait analysis. This paper presents a method to estimate the vGRF using inertial measurement units (IMU) and machine learning techniques. Four wearable IMUs were used to obtain flexion/extension angles of the hip, knee, and ankle joints, and an IMU placed over the C7 vertebra to measure vertical acceleration. We trained and compared the performance of two machine learning algorithms: feedforward neural networks (FNN) and random forest (RF). We investigated the importance of the inputs introduced into the models and analyzed in detail the contribution of lower limb kinematics and vertical acceleration to model performance. The results suggest that the inclusion of vertical acceleration increases the root mean square error in the FNN, while the RF appears to decrease it. We also analyzed the ability of the models to construct the force signal, with particular emphasis on the magnitude and timing of the vGRF peaks. Using the proposed method, we concluded that FNN and RF models can estimate the vGRF with high accuracy.

Hybrid brain/neural interface and autonomous vision-guided whole-arm exoskeleton control to perform activities of daily living (ADLs).

BACKGROUND: The aging of the population and the progressive increase of life expectancy in developed countries is leading to a high incidence of age-related cerebrovascular diseases, which affect people's motor and cognitive capabilities and might result in the loss of arm and hand functions. Such conditions have a detrimental impact on people's quality of life. Assistive robots have been developed to help people with motor or cognitive disabilities to perform activities of daily living (ADLs) independently. Most of the robotic systems for assisting on ADLs proposed in the state of the art are mainly external manipulators and exoskeletal devices. The main objective of this study is to compare the performance of an hybrid EEG/EOG interface to perform ADLs when the user is controlling an exoskeleton rather than using an external manipulator. METHODS: Ten impaired participants (5 males and 5 females, mean age 52 ± 16 years) were instructed to use both systems to perform a drinking task and a pouring task comprising multiple subtasks. For each device, two modes of operation were studied: synchronous mode (the user received a visual cue indicating the sub-tasks to be performed at each time) and asynchronous mode (the user started and finished each of the sub-tasks independently). Fluent control was assumed when the time for successful initializations ranged below 3 s and a reliable control in case it remained below 5 s. NASA-TLX questionnaire was used to evaluate the task workload. For the trials involving the use of the exoskeleton, a custom Likert-Scale questionnaire was used to evaluate the user's experience in terms of perceived comfort, safety, and reliability. RESULTS: All participants were able to control both systems fluently and reliably. However, results suggest better performances of the exoskeleton over the external manipulator (75% successful initializations remain below 3 s in case of the exoskeleton and bellow 5s in case of the external manipulator). CONCLUSIONS: Although the results of our study in terms of fluency and reliability of EEG control suggest better performances of the exoskeleton over the external manipulator, such results cannot be considered conclusive, due to the heterogeneity of the population under test and the relatively limited number of participants.

Patients' physiological reactions to competitive rehabilitation therapies assisted by robotic devices.

BACKGROUND: The aging of the population and the progressive increase in life expectancy in developed countries is leading to a high incidence of cerebrovascular diseases. Several studies have demonstrated that robot-assisted rehabilitation therapies combined with serious games can improve rehabilitation outcomes. Social interaction in the form of multiplayer games has been highlighted as a potential element to increase patient's motivation and exercise intensity, which professionals have described as one of the determining factors in maximizing rehabilitation outcomes. Despite this, it has not been widely studied. Physiological measures have been proven as an objective tool to evaluate patients' experience in robot-assisted rehabilitation environments. However, they have not been used to evaluate patients' experience in multiplayer robot-assisted rehabilitation therapies. The main objective of this study is to analyze whether the interpersonal interaction inherent in a competitive game mode affects the patients' physiological responses in robot-assisted rehabilitation environments. METHODS: A total of 14 patients participated in this study. The results of a competitive game mode were compared with a single-player game mode with different difficulty levels. Exercise intensity and performance were measured through parameters extracted from the game and the information provided by the robotic rehabilitation platforms. The physiological response of patients in each game mode was measured by the heart rate (HR) and the galvanic skin response (GSR). Patients were asked to fill out the IMI and the overall experience questionnaire. RESULTS: The exercise intensity results show that high-difficulty single-player game mode is similar in terms of intensity level to a competitive game mode, based on velocity values, reaction time and questionnaire results. However, the results of the physiological responses of the patients measured by GSR and HR are lower in the case of the competitive mode compared to the high-difficulty single-player game mode, obtaining results similar to those obtained in the low-difficulty single-player game mode. CONCLUSIONS: Patients find the competitive game mode the most fun, which is also the mode they report experiencing the most effort and stress level. However, this subjective evaluation is not in line with the results of physiological responses. This study concludes that interpersonal interaction inherent to a competitive game mode influences patients' physiological responses. This could mean that social interaction is an important factor to consider when interpreting the results obtained from physiological measurements.