Detecting human movement is crucial for the control of lower limb wearable robotics designed to assist daily activities or rehabilitation tasks. Sensorized insoles present a viable option for extracting control inputs, such as gait events and the corresponding phases, essential for regulating the magnitude and timing of assistance. Given their highly sensitive piezoelectric response to dynamic loading, ferroelectrets emerge as a cost-effective solution for customizing sensors suitable for these autonomous systems. Within this study, an insole with four ferroelectret sensors is 3D-printed monolithically from polylactic acid (PLA) onto bulk films of the same material through seamless thermal fusion. Sensor and insole are characterized through a testing machine and by conducting human walking experiments on an instrumented treadmill. The testing machine results indicate suitable sensor performance for the application in wearable robotics concerning the sensitivity, minimal detectable change, hysteresis, drift, and repeatability. Walking experiments reveal the insole’s capability to detect gait events such as heel strikes with minimal variability and on average 16 ms faster compared to the reference of vertical ground reaction forces across all walking speeds above 1 m/s. The peak sensor outputs strongly relate to the reference while both exhibit a linear (R-squared > 95%) increase corresponding to walking speed. In conclusion, study findings demonstrate the feasibility of PLA-based ferroelectrets as customized insole sensors for event detection in gait analysis, enabling assessment of human biomechanics with minimal impact on the natural gait and control of autonomous wearable robotics, such as exoskeletons.

Hand gestures are a natural form of human communication, making gesture recognition a sensible approach for intuitive human-computer interaction. Wearable sensors on the forearm can be used to detect the muscle contractions that generate these gestures, but classification approaches relying on a single measured modality lack accuracy and robustness. In this work, we analyze sensor fusion of force myography (FMG) and electromyography (EMG) for gesture recognition. We employ piezoelectric FMG sensors based on ferroelectrets and a commercial EMG system in a user study with 13 participants to measure 66 distinct hand movements with 10ms labelling precision. Three classification tasks, namely flexion and extension, single finger, and all finger movement classification, are performed using common handcrafted features as input to machine learning classifiers. Subsequently, the evaluation covers the effectiveness of the sensor fusion using correlation analysis, classification performance based on leave-one-subject-out-cross-validation and 5x2cv-t-tests, and its effects of involuntary movements on classification. We find that sensor fusion leads to significant improvement (42% higher average recognition accuracy) on all three tasks and that both sensor modalities contain complementary information. Furthermore, we confirm this finding using reduced FMG and EMG sensor sets. This study reinforces the results of prior research about the effectiveness of sensor fusion by performing meticulous statistical analyses, thereby paving the way for multi-sensor gesture recognition in assistance systems.

Recognizing human movement intention is key to control wearable robotics for assistance such as in the elderly and rehabilitation. Piezoelectric sensors, in particular highly sensitive ferroelectrets (FEs), qualify for the assessment of subtle muscle movements using force myography (FMG). In contrast to electromyography (EMG), which measures electrical muscle activity, FMG measures the mechanical muscle response to overcome issues in electrode placement and skin preparation. We employ 3D-printed polypropylene FE sensor patches for FMG adjacent to EMG electrodes as a reference at the lower limb of six young unimpaired participants performing sit-to-stand movements. The activity onsets of FMG and EMG are systematically compared at the quadriceps femoris and visually verified using a high-speed camera. The average time difference (35 $\pm$ 50 ms) aligns with the known electromechanical muscle delay. Notably, our results demonstrate that FMG can provide an early indicator for the activity onset significantly before the EMG (on average 263 $\pm$ 90 ms earlier) in this scenario due to the possibility to measure passive muscle deformation. In addition, the signal-to-noise ratio (SNR) of the FE signal (53 dB) exceeds that of the EMG (39 dB) during the maximum muscle activity, while the average variability is slightly larger for FE (22%) compared to EMG (17%). In conclusion, our customizable FE sensors prove to be suitable for motion intention detection and promise an advance in wearable assistive devices, such as active prostheses and exoskeletons, to support people in need.

People with physical disabilities in their upper extremities face serious issues in using classical input devices due to lacking movement possibilities and precision. This article suggests an alternative input concept and presents corresponding input devices. The proposed interface combines an inertial measurement unit and force sensing resistors, which can replace mouse and keyboard. Head motions are mapped to mouse pointer positions, while mouse button actions are triggered by contracting mastication muscles. The contact pressures of each fingertip are acquired to replace the conventional keyboard. To allow for complex text entry, the sensory concept is complemented by an ambiguous keyboard layout with ten keys. The related word prediction function provides disambiguation at word level. Haptic feedback is provided to users corresponding to their virtual keystrokes for enhanced closed-loop interactions. This alternative input system enables text input as well as the emulation of a two-button mouse.

Robotic arms are used in a wide range of industrial and medical applications. However, for research and education, users often face a trade-off between costly commercial solutions with no adaptability and open-source alternatives that lack usability and functionality. In education, this problem is exacerbated by the prohibitive cost of commercial systems or simplifications that distort learning. Thus, we present HELENE, an open-source robot with six degrees of freedom, closed-loop position control, and robot operating system (ROS) integration. The modular design of the robot, printed on a commercial 3D printer, and its integrated custom electronics allow for easy customization for research purposes. The joints are driven by standard stepper motors with closed-loop position control using absolute encoders. The ROS integration guarantees widespread control options and integration into existing environments. Our prototype, tested in accordance with ISO 9283, has a small positional accuracy error of 8.4 mm and a repeatability error of only 0.87 mm with a load capacity of 500 g at a reach of 432 mm. Ten prototypes were built and used in various research and education applications, demonstrating the versatile applicability of this open-source robot, closing the gap between reliable commercial systems and flexible open-source solutions.

Haptic motion guidance has the potential to advance assistive technologies that support humans in movement tasks. This study systematically evaluates wrist rotation guidance methods in a 2×2 repeated measures design using a wearable vibrotactile feedback device. In two tasks, we investigate the benefits of encoding the current target distance in the cue strength and conveying additional information about the target location by incorporating a tactile illusion known as phantom sensation. For a directional response task, we analyze reaction times and error rates, and for an angle targeting task, we examine rise time, settling time, and maximum overshoot of the normalized step responses. These objective criteria are complemented by subjective user ratings that assess the intuitiveness and ease of interpreting the vibrotactile cues. Feedback methods incorporating an adaptive amplitude perform significantly better in the angle targeting task compared to those using a constant amplitude. Additional improvements can be achieved by combining the adaptive amplitude with phantom sensations, including an average additional 24.3% reduction in rise time. Furthermore, more than half of the participants rate this combination as their favorite method. Altogether, the results underline the potential of incorporating phantom sensations in vibrotactile wrist guidance, thereby contributing to the advancement of wearable haptics in assistive applications.