17 research outputs found
Toward Unobtrusive In-home Gait Analysis Based on Radar Micro-Doppler Signatures
Objective: In this paper, we demonstrate the applicability of radar for gait
classification with application to home security, medical diagnosis,
rehabilitation and assisted living. Aiming at identifying changes in gait
patterns based on radar micro-Doppler signatures, this work is concerned with
solving the intra motion category classification problem of gait recognition.
Methods: New gait classification approaches utilizing physical features,
subspace features and sum-of-harmonics modeling are presented and their
performances are evaluated using experimental K-band radar data of four test
subjects. Five different gait classes are considered for each person, including
normal, pathological and assisted walks. Results: The proposed approaches are
shown to outperform existing methods for radar-based gait recognition which
utilize physical features from the cadence-velocity data representation domain
as in this paper. The analyzed gait classes are correctly identified with an
average accuracy of 93.8%, where a classification rate of 98.5% is achieved for
a single gait class. When applied to new data of another individual a
classification accuracy on the order of 80% can be expected. Conclusion: Radar
micro-Doppler signatures and their Fourier transforms are well suited to
capture changes in gait. Five different walking styles are recognized with high
accuracy. Significance: Radar-based sensing of human gait is an emerging
technology with multi-faceted applications in security and health care
industries. We show that radar, as a contact-less sensing technology, can
supplement existing gait diagnostic tools with respect to long-term monitoring
and reproducibility of the examinations.Comment: 11 pages, 6 figure
Detection of Gait Asymmetry Using Indoor Doppler Radar
Doppler radar systems enable unobtrusive and privacy-preserving long-term
monitoring of human motions indoors. In particular, a person's gait can provide
important information about their state of health. Utilizing micro-Doppler
signatures, we show that radar is capable of detecting small differences
between the step motions of the two legs, which results in asymmetric gait.
Image-based and physical features are extracted from the radar return signals
of several individuals, including four persons with different diagnosed gait
disorders. It is shown that gait asymmetry is correctly detected with high
probability, irrespective of the underlying pathology, for at least one motion
direction.Comment: 6 pages, 5 figures, 4 tables; accepted at the IEEE Radar Conference
2019, Boston, MA, US
Radar Human Motion Recognition Using Motion States and Two-Way Classifications
We perform classification of activities of daily living (ADL) using a
Frequency-Modulated Continuous Waveform (FMCW) radar. In particular, we
consider contiguous motions that are inseparable in time. Both the
micro-Doppler signature and range-map are used to determine transitions from
translation (walking) to in-place motions and vice versa, as well as to provide
motion onset and the offset times. The possible classes of activities post and
prior to the translation motion can be separately handled by forward and
background classifiers. The paper describes ADL in terms of states and
transitioning actions, and sets a framework to deal with separable and
inseparable contiguous motions. It is shown that considering only the
physically possible classes of motions stemming from the current motion state
improves classification rates compared to incorporating all ADL for any given
time
Doppler Radar for the Extraction of Biomechanical Parameters in Gait Analysis
The applicability of Doppler radar for gait analysis is investigated by
quantitatively comparing the measured biomechanical parameters to those
obtained using motion capturing and ground reaction forces. Nineteen
individuals walked on a treadmill at two different speeds, where a radar system
was positioned in front of or behind the subject. The right knee angle was
confined by an adjustable orthosis in five different degrees. Eleven gait
parameters are extracted from radar micro-Doppler signatures. Here, new methods
for obtaining the velocities of individual lower limb joints are proposed.
Further, a new method to extract individual leg flight times from radar data is
introduced. Based on radar data, five spatiotemporal parameters related to
rhythm and pace could reliably be extracted. Further, for most of the
considered conditions, three kinematic parameters could accurately be measured.
The radar-based stance and flight time measurements rely on the correct
detection of the time instant of maximal knee velocity during the gait cycle.
This time instant is reliably detected when the radar has a back view, but is
underestimated when the radar is positioned in front of the subject. The
results validate the applicability of Doppler radar to accurately measure a
variety of medically relevant gait parameters. Radar has the potential to
unobtrusively diagnose changes in gait, e.g., to design training in prevention
and rehabilitation. As contact-less and privacy-preserving sensor, radar
presents a viable technology to supplement existing gait analysis tools for
long-term in-home examinations.Comment: 13 pages, 9 figures, 2 tables, accepted for publication in the IEEE
Journal of Biomedical and Health Informatics (J-BHI
mmFall: Fall Detection using 4D MmWave Radar and a Hybrid Variational RNN AutoEncoder
In this paper we propose mmFall - a novel fall detection system, which
comprises of (i) the emerging millimeter-wave (mmWave) radar sensor to collect
the human body's point cloud along with the body centroid, and (ii) a
variational recurrent autoencoder (VRAE) to compute the anomaly level of the
body motion based on the acquired point cloud. A fall is claimed to have
occurred when the spike in anomaly level and the drop in centroid height occur
simultaneously. The mmWave radar sensor provides several advantages, such as
privacycompliance and high-sensitivity to motion, over the traditional sensing
modalities. However, (i) randomness in radar point cloud data and (ii)
difficulties in fall collection/labeling in the traditional supervised fall
detection approaches are the two main challenges. To overcome the randomness in
radar data, the proposed VRAE uses variational inference, a probabilistic
approach rather than the traditional deterministic approach, to infer the
posterior probability of the body's latent motion state at each frame, followed
by a recurrent neural network (RNN) to learn the temporal features of the
motion over multiple frames. Moreover, to circumvent the difficulties in fall
data collection/labeling, the VRAE is built upon an autoencoder architecture in
a semi-supervised approach, and trained on only normal activities of daily
living (ADL) such that in the inference stage the VRAE will generate a spike in
the anomaly level once an abnormal motion, such as fall, occurs. During the
experiment, we implemented the VRAE along with two other baselines, and tested
on the dataset collected in an apartment. The receiver operating characteristic
(ROC) curve indicates that our proposed model outperforms the other two
baselines, and achieves 98% detection out of 50 falls at the expense of just 2
false alarms.Comment: Preprint versio
How to create value with unobtrusive monitoring technology in home-based dementia care: a multimethod study among key stakeholders
BACKGROUND: There is a growing interest to support extended independent living of people with dementia (PwD) via unobtrusive monitoring (UM) technologies which allow caregivers to remotely monitor lifestyle, health, and safety of PwD. However, these solutions will only be viable if developers obtain a clear picture of how to create value for all relevant stakeholders involved and achieve successful implementation. The aim of this study was therefore to explore the value proposition of UM technology in home-based dementia care and preconditions for successful implementation from a multi-stakeholder perspective. METHODS: We conducted an expert-informed survey among potential stakeholders (n = 25) to identify key stakeholders for UM technology in home-based dementia care. Subsequently, focus groups and semi-structured interviews were conducted among 5 key stakeholder groups (n = 24) including informal caregivers (n = 5), home care professionals (n = 5), PwD (n = 4), directors and managers within home care (n = 4), and policy advisors within the aged care and health insurance sector (n = 6). The sessions addressed the value proposition- and business model canvas and were analyzed using thematic analysis. RESULTS: Stakeholders agreed that UM technology should provide gains such as objective surveillance, timely interventions, and prevention of unnecessary control visits, whereas pains mainly included information overload, unplannable care due to real-time monitoring, and less human interaction. The overall design-oriented need referred to clear situation classifications including urgent care (fall- and wandering detection), non-urgent care (deviations in eating, drinking, sleeping), and future care (risk predictions). Most important preconditions for successful implementation of UM technology included inter-organizational collaboration, a shared vision on re-shaping existing care processes, integrated care ICT infrastructures, clear eligibility criteria for end-users, and flexible care reimbursement systems. CONCLUSIONS: Our findings can guide the value-driven development and implementation of UM technology for home-based dementia care. Stakeholder values were mostly aligned, although stakeholders all had their own perspective on what UM technology should accomplish. Besides, our study highlights the complexity of implementing novel UM technology in home-based dementia care. To achieve successful implementation, organizational and financial preconditions, as well as digital data exchange between home care organizations, will be important. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-022-03550-1
Sequential human gait classification with distributed radar sensor fusion
This paper presents different information fusion approaches to classify human gait patterns and falls in a radar sensors network. The human gaits classified in this work are both individual and sequential, continuous gait collected by a FMCW radar and three UWB pulse radar placed at different spatial locations. Sequential gaits are those containing multiple gait styles performed one after the other, with natural transitions in between, including fall events developing from walking gait in some cases. The proposed information fusion approaches operate at signal and decision level. For the signal level combination, a simple trilateration algorithm is implemented on the range data from the 3 UWB radar sensors, achieving good classification results with the proposed Bi-LSTM (Bidirectional LSTM neural network) as classifier, without exploiting conventional micro-Doppler information. For the decision level fusion, the classification results of individual radars using the Bi-LSTM network are combined with a robust Naive Bayes Combiner (NBC), and this showed subsequent improvement compared to the single radar case thanks to multi-perspective views of the subjects. Compared to conventional SVM and Random Forest classifiers, the proposed approach yields +20% and +17% improvement in the classification accuracy of individual gaits for the range-only trilateration method and NBC decision fusion method, respectively. When classifying sequential gaits, the overall accuracy for the two proposed methods reaches 93% and 90%, with validation via a ’leaving one participant out’ approach to test the robustness with subjects unknown to the network