97 research outputs found
A Domain Agnostic Normalization Layer for Unsupervised Adversarial Domain Adaptation
We propose a normalization layer for unsupervised domain adaption in semantic
scene segmentation. Normalization layers are known to improve convergence and
generalization and are part of many state-of-the-art fully-convolutional neural
networks. We show that conventional normalization layers worsen the performance
of current Unsupervised Adversarial Domain Adaption (UADA), which is a method
to improve network performance on unlabeled datasets and the focus of our
research. Therefore, we propose a novel Domain Agnostic Normalization layer and
thereby unlock the benefits of normalization layers for unsupervised
adversarial domain adaptation. In our evaluation, we adapt from the synthetic
GTA5 data set to the real Cityscapes data set, a common benchmark experiment,
and surpass the state-of-the-art. As our normalization layer is domain agnostic
at test time, we furthermore demonstrate that UADA using Domain Agnostic
Normalization improves performance on unseen domains, specifically on
Apolloscape and Mapillary
Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction
Data analytics helps basketball teams to create tactics. However, manual data
collection and analytics are costly and ineffective. Therefore, we applied a
deep bidirectional long short-term memory (BLSTM) and mixture density network
(MDN) approach. This model is not only capable of predicting a basketball
trajectory based on real data, but it also can generate new trajectory samples.
It is an excellent application to help coaches and players decide when and
where to shoot. Its structure is particularly suitable for dealing with time
series problems. BLSTM receives forward and backward information at the same
time, while stacking multiple BLSTMs further increases the learning ability of
the model. Combined with BLSTMs, MDN is used to generate a multi-modal
distribution of outputs. Thus, the proposed model can, in principle, represent
arbitrary conditional probability distributions of output variables. We tested
our model with two experiments on three-pointer datasets from NBA SportVu data.
In the hit-or-miss classification experiment, the proposed model outperformed
other models in terms of the convergence speed and accuracy. In the trajectory
generation experiment, eight model-generated trajectories at a given time
closely matched real trajectories
Inertial Measurement Unit-Based Gait Event Detection in Healthy and Neurological Cohorts: A Walk in the Dark
A deep learning (DL)-based network is developed to determine gait events from IMU data from a shank- or foot-worn device. The DL network takes as input the raw IMU data and predicts for each time step the probability that it corresponds to an initial or final contact. The algorithm is validated for walking at different self-selected speeds across multiple neurological diseases and both in clinical research settings and the habitual environment. The algorithms shows a high detection rate for initial and final contacts, and a small time error when compared to reference events obtained with an optical motion capture system or pressure insoles. Based on the excellent performance, it is concluded that the DL algorithm is well suited for continuous long-term monitoring of gait in the habitual environment
No time to waste: practical statistical contact tracing with few low-bit messages
Pandemics have a major impact on society and the economy. In the case of a new virus, such as COVID-19, high-grade tests and vaccines might be slow to develop and scarce in the crucial initial phase. With no time to waste and lock-downs being expensive, contact tracing is thus an essential tool for policymakers. In theory, statistical inference on a virus transmission model can provide an effective method for tracing infections. However, in practice, such algorithms need to run decentralized, rendering existing methods – that require hundreds or even thousands of daily messages per person – infeasible. In this paper, we develop an algorithm that (i) requires only a few (2-5) daily messages, (ii) works with extremely low bandwidths (3-5 bits) and (iii) enables quarantining and targeted testing that drastically reduces the peak and length of the pandemic. We compare the effectiveness of our algorithm using two agent-based simulators of realistic contact patterns and pandemic parameters and show that it performs well even with low bandwidth, imprecise tests, and incomplete population coverage
A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts
Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement
unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms
often require knowledge about sensor orientation and use empirically derived thresholds. As align ment cannot always be controlled for in ambulatory assessments, methods are needed that require
little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep
learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked
5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on
the left and right ankle and shank. Gait events were detected and stride parameters were extracted
using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The
deep learning model consisted of convolutional layers using dilated convolutions, followed by two
independent fully connected layers to predict whether a time step corresponded to the event of
initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both
initial and final contacts across sensor locations (recall ≥ 92%, precision ≥ 97%). Time agreement
was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile
range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters
derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum
limits of agreement (−0.049 s, 0.051 s) for a 95% confidence level). Thus, the deep learning approach
was considered a valid approach for detecting gait events and extracting stride-specific parameters
with little knowledge on exact IMU location and orientation in conditions with and without walking
pathologies due to neurological diseases
Protect Your Score: Contact Tracing With Differential Privacy Guarantees
The pandemic in 2020 and 2021 had enormous economic and societal
consequences, and studies show that contact tracing algorithms can be key in
the early containment of the virus. While large strides have been made towards
more effective contact tracing algorithms, we argue that privacy concerns
currently hold deployment back. The essence of a contact tracing algorithm
constitutes the communication of a risk score. Yet, it is precisely the
communication and release of this score to a user that an adversary can
leverage to gauge the private health status of an individual. We pinpoint a
realistic attack scenario and propose a contact tracing algorithm with
differential privacy guarantees against this attack. The algorithm is tested on
the two most widely used agent-based COVID19 simulators and demonstrates
superior performance in a wide range of settings. Especially for realistic test
scenarios and while releasing each risk score with epsilon=1 differential
privacy, we achieve a two to ten-fold reduction in the infection rate of the
virus. To the best of our knowledge, this presents the first contact tracing
algorithm with differential privacy guarantees when revealing risk scores for
COVID19.Comment: Accepted to The 38th Annual AAAI Conference on Artificial
Intelligence (AAAI 2024
Changes in Coordination and Its Variability with an Increase in Functional Performance of the Lower Extremities
Clinical gait analysis has a long-standing tradition in biomechanics. However, the use of kinematic data or segment coordination has not been reported based on wearable sensors in “real-life” environments. In this work, the skeletal kinematics of 21 healthy and 24 neurogeriatric participants was collected in a magnetically disturbed environment with inertial measurement units (IMUs) using an accelerometer-based functional calibration method. The system consists of seven IMUs attached to the lower back, the thighs, the shanks, and the feet to acquire and process the raw sensor data. The Short Physical Performance Battery (SPPB) test was performed to relate joint kinematics and segment coordination to the overall SPPB score. Participants were then divided into three subgroups based on low (0–6), moderate (7–9), or high (10–12) SPPB scores. The main finding of this study is that most IMU-based parameters significantly correlated with the SPPB score and the parameters significantly differed between the SPPB subgroups. Lower limb range of motion and joint segment coordination correlated positively with the SPPB score, and the segment coordination variability correlated negatively. The results suggest that segment coordination impairments become more pronounced with a decreasing SPPB score, indicating that participants with low overall SPPB scores produce a peculiar inconsistent walking pattern to counteract lower extremity impairment in strength, balance, and mobility. Our findings confirm the usefulness of SPPB through objectively measured parameters, which may be relevant for the design of future studies and clinical routines
Quantification of Arm Swing during Walking in Healthy Adults and Parkinson's Disease Patients: Wearable Sensor-Based Algorithm Development and Validation
Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance. This study developed and validated a wearable sensor-based arm swing algorithm for healthy adults and patients with Parkinson's disease (PwP). Arm swings of 15 healthy adults and 13 PwP were evaluated (i) with wearable sensors on each wrist while walking on a treadmill, and (ii) with reflective markers for optical motion capture fixed on top of the respective sensor for validation purposes. The gyroscope data from the wearable sensors were used to calculate several arm swing parameters, including amplitude and peak angular velocity. Arm swing amplitude and peak angular velocity were extracted with systematic errors ranging from 0.1 to 0.5° and from -0.3 to 0.3°/s, respectively. These extracted parameters were significantly different between healthy adults and PwP as expected based on the literature. An accurate algorithm was developed that can be used in both clinical and daily-living situations. This algorithm provides the basis for the use of wearable sensor-extracted arm swing parameters in healthy adults and patients with movement disorders such as Parkinson's disease
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