Estimating qualitative parameters for assessment of body balance and arm function in a simulated ambulatory setting

Continuous daily-life monitoring of balance control and arm function of stroke survivors in an ambulatory setting, is essential for optimal guidance of rehabilitation. In a simulated ambulatory setting, balance and arm function of seven stroke subjects is evaluated using on-body measurement systems (Xsens MVN Biomech and the Xsens Instrumented Force Shoes). Ethical approval for this study was obtained. Qualitative parameters of body balance and arm function are estimated and compared with the results of a generally accepted clinical balance assessments (e.g. Berg Balance Scale and Fugl-Meyer)

Assessment and visualisation of daily-life arm movements after stroke

For an optimal guidance of the rehabilitation therapy of stroke patients in an in-home setting, objective, and patient specific assessment of upper extremity task performance is needed. Towards this goal, metrics of hand position relative to the pelvis were estimated and visualized. Metrics, including work area and maximum reaching distance, appeared to strongly correlate with the upper extremity part of the Fugl-Meyer Assessment scale (r>0.84, p<0.001). Proposed metrics and visualisation can be used to objectively assess the arm movement performance over a longer period of time in a daily-life setting, if combined with info about performed task derived from a activity monitor

Speckle Vibrometry for Instantaneous Heart Rate Monitoring

Instantaneous heart rate (IHR) has been investigated for sleep applications, such as sleep apnea detection and sleep staging. To ensure the comfort of the patient during sleep, it is desirable for IHR to be measured in a contact-free fashion. In this work, we use speckle vibrometry (SV) to perform on-skin and on-textile IHR monitoring in a sleep setting. Minute motions on the laser-illuminated surface can be captured by a defocused camera, enabling the detection of cardiac motions even on textiles. We investigate supine, lateral, and prone sleeping positions. Based on Bland–Altman analysis between SV cardiac measurements and electrocardiogram (ECG), with respect to each position, we achieve the best limits of agreement with ECG values of [−8.65, 7.79] bpm, [−9.79, 9.25] bpm, and [−10.81, 10.23] bpm, respectively. The results indicate the potential of using speckle vibrometry as a contact-free monitoring method for instantaneous heart rate in a setting where the participant is allowed to rest in a spontaneous position while covered by textile layers

Deep Transfer Learning for Automated Single-Lead EEG Sleep Staging with Channel and Population Mismatches

Automated sleep staging using deep learning models typically requires training on hundreds of sleep recordings, and pre-training on public databases is therefore common practice.However, suboptimal sleep stage performance may occur from mismatches between source and target datasets, such as differences in population characteristics (e.g., an unrepresented sleep disorder) or sensors (e.g., alternative channel locations for wearable EEG). We investigated three strategies for training an automated single-channel EEG sleep stager: pre-training (i.e., training on the original source dataset), training-from-scratch (i.e., training on the new target dataset), and fine-tuning (i.e., training on the original source dataset, fine-tuning on the new target dataset). As source dataset, we used the F3-M2 channel of healthy subjects (N=94). Performance of the different training strategies was evaluated using Cohen's Kappa (κ) in eight smaller target datasets consisting of healthy subjects (N=60), patients with obstructive sleep apnea (OSA, N=60), insomnia (N=60), and REM sleep behavioral disorder (RBD, N=22), combined with two EEG channels, F3-M2 and F3-F4. No differences in performance between the training strategies was observed in the agematched F3-M2 datasets, with an average performance across strategies of κ = .83 in healthy, κ = .77 in insomnia, and κ = .74 in OSA subjects. However, in the RBD set, where data availability was limited, fine-tuning was the preferred method (κ = .67), with an average increase in κ of .15 to pre-training and training-from-scratch. In the presence of channel mismatches, targeted training is required, either through training-from-scratch or fine-tuning, increasing performance with κ = .17 on average. We found that, when channel and/or population mismatches cause suboptimal sleep staging performance, a fine-tuning approach can yield similar to superior performance compared to building a model from scratch, while requiring a smaller sample size. In contrast to insomnia and OSA, RBD data contains characteristics, either inherent to the pathology or age-related, which apparently demand targeted training

Deep Transfer Learning for Automated Single-Lead EEG Sleep Staging with Channel and Population Mismatches

Automated sleep staging using deep learning models typically requires training on hundreds of sleep recordings, and pre-training on public databases is therefore common practice.However, suboptimal sleep stage performance may occur from mismatches between source and target datasets, such as differences in population characteristics (e.g., an unrepresented sleep disorder) or sensors (e.g., alternative channel locations for wearable EEG). We investigated three strategies for training an automated single-channel EEG sleep stager: pre-training (i.e., training on the original source dataset), training-from-scratch (i.e., training on the new target dataset), and fine-tuning (i.e., training on the original source dataset, fine-tuning on the new target dataset). As source dataset, we used the F3-M2 channel of healthy subjects (N=94). Performance of the different training strategies was evaluated using Cohen's Kappa (κ) in eight smaller target datasets consisting of healthy subjects (N=60), patients with obstructive sleep apnea (OSA, N=60), insomnia (N=60), and REM sleep behavioral disorder (RBD, N=22), combined with two EEG channels, F3-M2 and F3-F4. No differences in performance between the training strategies was observed in the agematched F3-M2 datasets, with an average performance across strategies of κ = .83 in healthy, κ = .77 in insomnia, and κ = .74 in OSA subjects. However, in the RBD set, where data availability was limited, fine-tuning was the preferred method (κ = .67), with an average increase in κ of .15 to pre-training and training-from-scratch. In the presence of channel mismatches, targeted training is required, either through training-from-scratch or fine-tuning, increasing performance with κ = .17 on average. We found that, when channel and/or population mismatches cause suboptimal sleep staging performance, a fine-tuning approach can yield similar to superior performance compared to building a model from scratch, while requiring a smaller sample size. In contrast to insomnia and OSA, RBD data contains characteristics, either inherent to the pathology or age-related, which apparently demand targeted training

Gait analysis using ultrasound and inertial sensors

Introduction and past research:\ud Inertial sensors are great for orientation estimation, but they cannot measure relative positions of human body segments directly. In previous work we used ultrasound to estimate distances between body segments [1]. In [2] we presented an easy to use system for gait analysis in clinical practice but also in-home situations. Ultrasound range estimates were fused with data from foot-mounted inertial sensors, using an extended Kalman filter, for 3D (relative) position and orientation estimation of the feet.\ud \ud Validation:\ud From estimated 3D positions we calculated step lengths and stride widths and compared this to an optical reference system for validation. Mean (±standard deviation) of absolute differences was 1.7 cm (±1.8 cm) for step lengths and 1.2 cm (±1.2 cm) for stride widths when comparing 54 walking trials of three healthy subjects.\ud \ud Clinical application:\ud Next, the system presented in [2] was used in the INTERACTION project, for measuring eight stroke subjects during a 10 m walk test [3]. Step lengths, stride widths and stance and swing times were compared with the Berg balance scale score. The first results showed a correlation between step lengths and Berg balance scale scores. To draw real conclusions, more patients and also different activities will be investigated next.\ud \ud Future work:\ud In future work we will extend the system with inertial sensors on the upperand lower legs and the pelvis, to be able to calculate a closed loop and improve the estimation of joint angles compared with systems containing only inertial sensors

Ambulatory Estimation of Relative Foot Positions using Ultrasound

The recording of human movement is used for biomedical applications like physical therapy and sports training. Over the last few years inertial sensors have been proven to be a useful ambulatory alternative to traditional optical systems. An example of a successful application is the instrumented shoe, which contains two 6D force/moment sensors beneath the heel and the forefoot and two inertial sensors rigidly attached to the force/moment sensors [1]. These shoes can be used for ambulatory assessment of walking kinetics and kinematics. The relative position of the feet is currently not measured directly but estimated from double integration of feet accelerations. However, this method immediately leads to large position errors (drift) when the estimated inertial accelerations are inaccurate. In this study we investigated the ambulatory estimation of the relative positions of the feet using ultrasound transducers. On one shoe we mounted a 400PT120 Air Ultrasonic Ceramic Transducer (13 mm diameter, 10 mm height, 85º beam angle) sending a 40 kHz pulse to a similar transducer on the other shoe. Using the time of flight, the distance is estimated. Under static conditions a mean error of 5.7 ±0.8 mm was obtained over a range of 5 till 75 cm [2]. From this pilot study we concluded that the distance between the feet can be estimated ambulatory using small and low-cost ultrasound transducers. Future research includes the use of multiple transducers on each foot for a distance measure during different daily-life activities. Also the relative positions of the feet will be investigated by fusing the distance estimates with inertial sensor data