Several strategies have been proposed to improve patient motivation and exercise intensity during robot-aided stroke rehabilitation. One relatively unexplored possibility is two-player gameplay, allowing subjects to compete or cooperate with each other to achieve a common goal. In order to explore the potential of such games, we designed a two-player game played using two ARMin arm rehabilitation robots.

Previous studies have presented algorithms for detection of turns during gait using wearable sensors, but those algorithms were not built for real-time use. This paper therefore investigates the optimal approach for real-time detection of planned turns during gait using wearable inertial measurement units. Several different sensor positions (head, back and legs) and three different detection criteria (orientation, angular velocity and both) are compared with regard to their ability to correctly detect turn onset. Furthermore, the different sensor positions are compared with regard to their ability to predict the turn direction and amplitude. The evaluation was performed on ten healthy subjects who performed left/right turns at three amplitudes (22, 45 and 90 degrees). Results showed that turn onset can be most accurately detected with sensors on the back and using a combination of orientation and angular velocity. The same setup also gives the best prediction of turn direction and amplitude. Preliminary measurements with a single amputee were also performed and highlighted important differences such as slower turning that need to be taken into account.

This paper uses physiological measurements to estimate human workload and effort in physical human–robot interaction. Ten subjects performed 19 consecutive task periods using the ARMin robot while difficulty was varied along two scales. Three physiological modalities were measured: electroencephalography, autonomic nervous system (ANS) responses (electrocardiography, skin conductance, respiration, skin temperature) and eye tracking. After each task period, reference workload and effort values were collected using the NASA Task Load Index. Machine learning was used to estimate workload and effort from physiological data. All three physiological modalities performed significantly better than random, particularly using nonlinear estimation algorithms. The most important ANS responses were respiration and skin conductance, while the most important electroencephalographic information was obtained from frontal and central sites. However, all three physiological modalities were outperformed by task performance and movement data. This suggests that future studies should try to demonstrate advantages of physiological measurements over other information sources.

Rapid recognition of voluntary motions is crucial in human-computer interaction, but few studies compare the predictive abilities of different sensing technologies. This paper thus compares performances of different technologies when predicting targets of human reaching motions: electroencephalography (EEG), electrooculography, camera-based eye tracking, electromyography (EMG), hand position, and the user's preferences. Supervised machine learning is used to make predictions at different points in time (before and during limb motion) with each individual sensing modality. Different modalities are then combined using an algorithm that takes into account the different times at which modalities provide useful information. Results show that EEG can make predictions before limb motion onset, but requires subject-specific training and exhibits decreased performance as the number of possible targets increases. EMG and hand position give high accuracy, but only once the motion has begun. Eye tracking is robust and exhibits high accuracy at the very onset of limb motion. Several advantages of combining different modalities are also shown, including advantages of combining measurements with contextual data. Finally, some recommendations are given for sensing modalities with regard to different criteria and applications. The information could aid human-computer interaction designers in selecting and evaluating appropriate equipment for their applications.