MetaPhys: Few-Shot Adaptation for Non-Contact Physiological Measurement
Authors: Xin Liu
Ziheng Jiang
Joshua Wolff Fromm
Xuhai Xu
Shwetak Patel
Daniel McDuff
Venue:  (2021)
Keywords: Healthcare
Meta Learning
Computer Vision

Abstract: There are large individual differences in physiological processes, making designing personalized health sensing algorithms challenging. Existing machine learning systems struggle to generalize well to unseen subjects or contexts, especially in video-based physiological measurement. Although fine-tuning for a user might address this issue, it is difficult to collect large sets of training data for specific individuals because supervised algorithms require medical-grade sensors for generating the training target. Therefore, learning personalized or customized models from a small number of unlabeled samples is very attractive as it would allow fast calibrations. In this paper, we present a novel meta-learning approach called MetaPhys for learning personalized cardiac signals from 18-seconds of video data. MetaPhys works in both supervised and unsupervised manners. We evaluate our proposed approach on two benchmark datasets and demonstrate superior performance in cross-dataset evaluation with substantial reductions (42% to 44%) in errors compared with state-of-the-art approaches. Visualization of attention maps and ablation experiments reveal how the model adapts to each subject and why our proposed approach leads to these improvements. We have also demonstrated our proposed method significantly helps reduce the bias in skin type.  


Under review as a conference paper at ICLR 2021
METAPHYS: FEW-SHOT ADAPTATION FOR NON- CONTACT PHYSIOLOGICAL MEASUREMENT
Anonymous authors Paper under double-blind review
ABSTRACT
There are large individual differences in physiological processes, making designing personalized health sensing algorithms challenging. Existing machine learning systems struggle to generalize well unseen subjects or contexts, especially in video-based physiological measurement. Although fine-tuning for a user might address this issue, it is difficult to collect large sets of training data for specific individuals because supervised algorithms require medical-grade sensors for gener- ating the training target. Therefore, learning personalized or customized models from small number of unlabeled samples is very attractive as it would allow fast calibrations. In this paper, we present a novel meta-learning approach called MetaPhys for learning personalized cardiac signals from 18-seconds of video data. MetaPhys works in both supervised and unsupervised manners. We evaluate our proposed approach on two benchmark datasets and demonstrate superior perfor- mance in cross-datase evaluation with substantial reductions (42% to 44%) in errois compared with state-of-the-ae approaches. Visualization of attention maps and ablation experiments reveal how the model adapts to each subject and why our proposed approach leads to these improvements. We have also demonstrated our proposed method significantly helps reduce the bias in skin type.
INTRODUCTION
The importance of scalable health sensing has been acutely highlighted during the SARS-CoV-2 (COVID-19) pandemic. The virus has been linked increased risk of myocarditis and other serious cardiac (heart) conditions (Puntmann et al. (020). Contact sensors (elcctrocardiograms, oximeters) are the current gold-standard for measurement of heart function. However, these devices are still not ubiquitously available, especially in low-resource settings. The development of video- based contactless sensing of vital signs presents an opportunity for highly scalable physiological monitoring. Furthermore, in clinical settings non-contact sensing could reduce the risk of infection for vulnerable patients (e.g., infants and elderly) and the discomfort caused to them (Villarroel et al., 2019).
While there are compelling advantages of camera- based sensing, the approach also presents unsolved challenges. The use of ambient illumination means camera- measurement is sensitive to environmental differences in the intensity and composition of the incident light. Camera sensor differences mean that hardware can differ in sensitivity across the frequency spectrum. People (the subjects) exhibit large individual differences in appearance (e.g., skin type, facial hair) and physiology (e.g, pulse dynamics). Finally, contextual differences mean that motions in video at test time might be different from those seen in the training data. One specific example is that there exists biases in performance across skin types) Nowara et al. (2020). This problem is not isolated to physiological measurement as studies have found systematic biases in facial gender classification, with error rates up to 7x higher on women than men and poorer performance
people with darker skin types (Buolamwini Gebru 2018). Moreover, there are several challenges in collecting large corpora of high-quality physiological data: 1) recruiting and instrumenting participants is often expensive and requires advanced technical expertise, 2) the data can reveal the identity of the subjects and/or sensitive health information meaning it is difficult for researchers to share such datasets. Therefore, training supervised models that generalize well across environments and subjects challenging. For these reasons observe that performance cross-dataset evaluation is significantly worse than evaluation using current state-of- ofte--o-ttee the-art art methods (Chen & McDuff 2018) Liu et al.
2020).

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Calibration of consumer health sensors is often performed in clinic, where clinician will collect readings from a high-end sensor to calibrate a consumer-level device the patient owns. The reason for this is partly due to the variability within readings from consumer devices across different individuals. Ideally, we would be able to train a personalized model for each individual; however, standard supervised learning training schemes require large amounts of labeled data. Getting enough physiological training data of each individual is difficult because it requires using medical-grade devices to provide reliable labels. Being able to generate personalized model from small amount of training samples would enable customization based on few seconds or minutes of video captured while visiting clinic where people have access to gold-standard device. Furthermore, if this process could be achieved without even the need for these devices (i.e., in an unsupervised manner), that would have even greater impact. Finally, combining remote physiological measurement with telehealth could provide patients' vital signs for clinicians during remote diagnosis. Given that requests for telchealth appointments have increased more than 10x during COVID-19, and that this is expected to continue into the future (Smith et al. 2020), robust personalized models are of growing importance.
Meta-learning, or learning to learn, has been extensively studied in the past few years (Hospedales eet al.] 2020). Instead of learning a specific generalized mapping. the goal of meta-learning is to design model that can adapt a new task context with small amount of data. Due to the inherent ability for fast adaption, meta-learning is a good candidate strategy for building personalized models (e.g.. personalization in dialogue and video retargeting (Madotto et al./ 2019 Lee al. 2019).) However, we argue that meta learning is underused in healthcare where clinicians can quickly adapt their clinical knowledge to different patients. The goal of this work is develop meta-learning based personalization framework in remote physiological measurement with a limited amount of data from an unseen individual (task) to mimic how a clinician manually calibrates sensor readings for specific patient. When meta-learning is applied to remote physiological measurement, there are two kinds of scenarios: 1) supervised adaptation with few samples of labeled data from clinical grade sensor and 2) unsupervised adaptation with unlabeled data. We hypothesize that supervised adaptation is more likely to yield a robust personalized model with only a few labels, while unsupervised adaptation may personalize the model less effectively but with much lower effort and complexity.
In this paper, we propose a novel meta-learning approach to address the aforementioned challenges called MetaPhys. Our contributions are: 1) A meta-learning based deep neural framework, supporting both supervised and unsupervised few-shot adaptation, for camera- camera-based vital sign measurement; 2) A systematic cross-dataset evaluation showing that our system considerably outperforms the state-of-the-arr (42% to 52% reduction in heart rate error); 3) To perform an ablation experiment, freezing weights in the temporal and appearance branches to test sensitivity during adaptation: 4) An analysis of performance for subjects with different skin types. Our code, example models, and video results can be found on our github page
BACKGROUND
Video-Based Physiological Measurement: Video-based physiological measurement is a growing interdisciplinary domain that leverages ubiquitous imaging devices (e.g., webcams, smartphones' cameras) to measure vital signs and other physiological processes. Early work established that changes in light reflected from the body could be used to capture subtle variations blood volume and motion related to the photoplethysmooram (PPG) (Takano & Ohtal 2007]] /errruysse et al., 2008) and ballistocardiogram (BCG) (Balakrishnan al. 2013), respectively. Video analysis enables non-contact, spatial and temporal measurement of arterial and peripheral pulsations and allows for magnification of theses signals (Wu al., 2012), which may help with examination (e.g.. (Abnousi) ct al. 2019)). Based on the PPG and BCG signal, heart rate can be extracted (Poh et al. 2010b Balakrishnan al. 2013).
However, the relationship between pixels and underlying physiological changes in a video is complex and neural models have shown strong performance compared to source separation techniques (Chen & McDuff 2018) Yu et al. 2019 Zhan et al.) 2020). Conventional supervised learning requires a large amount of training data to produce a generalized model. However, obtaining large body of physiological and facial data is complicated and expensive. Current public datasets have limited
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numbers of subjects and diversity in regards of appearance (including skin type), camera sensors, environmental conditions and subject motions. Therefore, if the subject of interest is not in the training data or the video is otherwise different, performance can be considerably degraded, result that is not acceptable for physiological sensor.
Lee al. (Lee al. 2020) recognized the potential for meta-learning applied to imaging- based cardiac pulse measurement. Their method (Meta-rPPG) focuses on using unsupervised meta-learning and LSTM encoder-decoder architecture which our knowledge was not validated in previous work. Instead, our proposed meta-learning framework is built on top of a state-of-the-ang on-device network (Liu et al., 2020) and aims to explore the potential of both supervised and unsupervised on- device personalized meta-learning. Meta-rPPG uses a synthetic gradient generator and prototypical distance minimizer to perform transductive inference to enable unsupervised meta-learning. This learning mechanism requires number of rather complex steps. We propose a relatively simpler mechanism that is physiologically and optically grounded (Wang et al. 2016 achieves greater accuracy.
Liu al. (020) and
Meta-Learning and Person Specific Models:
The ability to learn from a small number of samples or observations is often used as an example of the unique capabilities of human intelligence. However, machine learning systems are often brittle in a similar context. Meta-learning approaches tackle this problem by creating general learner that is able to adapt to new task with small number of training samples, inspired by how humans can often master a new skill without many observations (Hospedales et al., (2020). However, most of the previous work in meta-learning focuses supervised vision problems (Zoph et al. 2018 Snell et al. been applied to image analysis (Vinyals al.
2017) and in the computer vision literature has mainly 2016 Li al. 2017). Supervised regression in video settings has received less attention. One of few examples is object or face tracking (Choi et al. 2019 Park & Berg, 2018). In these tasks, the learner needs to adapt to the individual differences in appearance of the target and then track it across frames, even if the appearance changes considerably over time in the video. [Choi et al. (2019) present a matching network architecture providing the meta-learner with information in the form of loss gradients obtained using the training samples.
The property of fast adaptation makes meta-learning good candidate for personalizing models, has been used in various applications such dialogue agents (Madotto et al. 2019). gaze estimation (He et al. 2019), sleep stage classification (Banluesombatkul et al. 2020), activity recognition (Gong let al. 2019). and video retargeting (Lee et al. (2019). For example, Banluesombatkul et al. proposed a MAML- meta-learning system to perform fast adaption of a sleep stage classification model using biosignals (Banluesombatkul et al. 2020). More recently, MetaPix (Lee al.. 2019) leveraged a meta-learning training schema with a small amount of video to adapt a universal generator to particular background and human in the problem of video retargeting. Similarly, our proposed meta-lcarning framework is also capable of personalizing universal remote physiological model to a new person an environmental setting.
3 METHOD
3.1 PHYSIOLOGICAL META-LEARNING
In camera-based cardiac measurement, the goal is separate pixel changes due to volumetric variations in blood and pulsatile motions from other variations that are not related to the pulse signal. Examples of "noise" in this context that might impact the performance on the task include: changes in the environment (illumination) and changes in appearance of the subject and motions (e.g., facial expressions, rigid head motions). A model trained within a traditional supervised learning regime might perform well if illumination, non-pulsatile motions, and appearances in the test set are similar to those in the training set. However, empirical evidence shows that performance usually significantly degrades from one dataset to another, suggesting that traditional training is likely to overfit to the training set to some extent (Chen & McDuff 2018). Therefore, to achieve statc-of-the-ee performance cross-dttaed evaluation, the system should have: 1) good
in remote physiological measurement initial representation of the mapping from the raw video data to pulse signal, and 2) a strategy for adapting unseen individuals and environments.
To achieve this, we propose a system called MetaPhys, an adaptable meta-learning based on-device framework aimed at efficient and personalized remote physiological sensing. MetaPhys uses
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Support Set
TS-CAN
Preling Midaclani
Pooking Middea
Rr Cont. netta
POS Deriiiing
Dropoct
Dispit
POS
Mask
Mask
Tatel
Averaged Frams
POS
Cony, Layer
Penting
Coftee Layerr (2c)
Training
Individual Query Set
Eval
V (
Support Loss (Meee Squared Dver
18s
Network
POS
Individual Support Set
Pt
Quary LOOS
Test Loss Chean Siaardd Evord
θ
WwiMWWW
Pulse Extinate
VL f(0z)
Figure I: We present MetaPhys, an approach for few-shot unsupervised adaptation for personalized camera-based physiological measurement models.
pretrained convolutional attention network as the backbone (described below) and leverages novel personalized meta-learning schema to overcome the aforementioned limitations. We adopt Model- Agnostic Meta-Learning (MAML) (Finn et al.) 2017) as personalized parameter update schema. MAML produces a general initialization as the starting point for fast adaptation to diverse set of unseen tasks with only few training samples. However, applying MAML the task of camera-based physiological measurement has differences to many previously explored meta-learning problems. Existing veta-learning approaches are often evaluated on classification or some toy regression tasks due to the lack of regression benchmark datasets (Hospedales et al., 2020). Our problem is non- trivial vision-based regression task due to the subtle nature of the underlying physiological signal. Algorithm outlines the training process for MetaPhys, we first pretrain the backbone network to get an initial spatial-tempora representation. Then we treat each individual as a task T, During the training. we split the data into support set (K video frames) and query set (K' video frames) for each individual (task). The support set is used to update the task's 's parameters and yield a personalized model 6g. The query set is used to assess the effectiveness of the personalized model and further update the global initialization 0 to make future adaptation better. A robust personalized model 0; aims provide a more accurate attention mask the corresponding motion branch and to preform precise physiological measurement for the target individual as well as the target's 's environment. During the testing stage, MetaPhys has the updated global initialization 6, and can generate é for each test individual (task) by optimizing the test support set as O2, + 0 a a With this training and testing schema, the robust global initialization i generated from MetaPhys not only leverages the pretrained representation but also learns how to adapt to individual and environmental noise quickly.
3.2 SPATIAL AND TEMPORAL. MODEL ARCHITECTURE BACKBONE
Our ultimate goal computationally efficient on-device meta-learning framework that offers inference at 150 fps. Therefore, we adopt the tate-of-thhe-rt architecture (TS-CAN) (Liu et al.) 2020) for remote cardiopulmonary monitoring. TS-CAN an end-to-end neural architecture with appearance and motion branches. The inputs are video frames and the output is the first-derivative of the pulse estimate. Tensor shifting modules (TSM) (Lin et al. 2019) are used that shift frames along the temporal axis allowing for information exchange across time. This helps capture temporal dependencies beyond consecutive frames. The appearance branch and attention mechanism help guide the motion branch focus on regions with high pulsatile signal (e.g., skin) instead of others (e.g..
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clothes, hair) (see Fig. D.. However, discover empirically that this network does not necessarily generalize well across datasets with differences in subjects, lighting, backgrounds and motions (see Table D One of the main challenges when employing TS-CAN is that the appearance branch may not generate an accurate mask while testing on unseen subjects or environments because of the differences in appearance of skin pixels. Without good attention mask, motions from other sources are likely to be given more weight, thus damaging the quality of our physiological estimate.
3.3 SUPERVISED OR UNSUPERVISED LEARNING
We explore both supervised and unsupervised training regimes for MetaPhys. Supervised personaliza- tion may be suitable in clinical settings that require highly precise adaptation and where there is access to reference devices. Unsupervised personalization may be preferable for consumer measurement when convenience and scalability is of greater priority and calibration with clinical grade device might be difficult.
For the supervised version of MetaPhys use the gold standard reference signal from a finger PPG or blood pressure wave (BPW) to train the meta-leamer and perform few-shot adaptation when testing. In contrast to the supervised version, in the unsupervised case the training of the MetaPhys meta-learner and parameter updates rather than the ground-truth signal from the medical-grade devices. We use ahysiologicalll-baseee unsupervised remote physiological measurement model to generate pseudo pulse signal estimates without relying on gold standard (POS) (Wang al.
pscudo labels during
measurements. More specifically, we leverage the Plane- Srato--Ottooollto--Saae 2016) method, which is the current state-of-the-art for demixing in this context. POS calculates projection plane orthogonal the skin-tone, derived based on optical and physiological principles. that is then used for pulse extraction. In details, POS can be summarized into four steps: 1) spatial averaging each frame, 2) temporal normalization within certain window size, 3) applying fixed matrix projection to offset the specular reflections and other noise, 4) band-pass filtering.
We observe that even though our unsupervised model uses the POS signal for meta-training, Meta- Phys's performance significantly outperforms POS once trained. As Algorithm 1 illustrates, the pseudo label generator G produces pseudo labels for both support frames and K' query frames for adaptation and parameter updates. We used pseudo labels for the query set (K') in training, as we observed similar empirical results in preliminary testing whether we used pseudo labels or ground-truth -tuut labels.
Algorithm MetaPhys: Meta-learning for physiological signal personalization
Require: S: Subject-wise video data
Require: batch personalized tasks where each task т; contains N data points from S, Require: A pscudo label generator G' for unsupervised meta-learning
1: < Pre-training TS-CAN on AFRL dataset
2: for each T; do if Supervised then
3:
4:
K <- Sample K support frames from videos of with ground truth labels K" <- Sample K" query frames from videos of r; with ground truth labels
5:
6:
else
7:
K < Sample K support frames from videos of T, with pseudo labels from G
8:
K' Sample K' query frames from videos of T3 with pseudo labels from G
9:
end if
0-.- +- a VoL= 8). Update the personalized params. based on indiv. support loss
11:
end for
12: 0
BV Σx, (K', 0z.), Update the global params. based on individuals' query loss
EXPERIMENTS
4.1 DATASETS
AFRL (Estepp et al. 2014): 300 videos of 25 participants (17 males) were recorded at 658x492 resolution and 30 fps. Pulse measurements were recorded via contact reflectance PPG sensor
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Table 1: Pulse Measurement the MMSE-HR and UBFC datasets.
Method
Train / Test Datasets
MAE
RMSE
SNR
p
Pretrain + Unsupervised MetaPhys
(AFRL UBFC) / MMSE
1.87
3.12
5.04
0.89
Pretrain Supervised MetaPhys
(AFRL UBFC) / MMSE
2.98
4.86
3.81
0.72
MetaPhys (No pretrain)
(AFRL UBFC)/ / MMSE
3.67
5.50
2.41
0.70
Supervised Pretrain + FT on Test Support Set
(AFRL & UBFC) / MMSE
4.05
5.68
2.76
0.76
Supervised Pretrain (Liu al. 2020
(AFRL UBFC) / MMSE
3.78
5.75
2.67
0.77
(Unsupervised) CHROM (De Haan & Jeanne
2013
None MMSE
3.2
5.71
5.42
0.75
(Unsupervised) POS Wang et al
2016
None / MMSE
3,98
6.66
5.74
0.67
(Unsupervised) ICA (Poh et al.
2010a
None / MMSE
4.12
6.46
6.09
0.67
Method
Train Test Datasets
MAE
RMSE
SNR
Pretrain + Unsupervised MetaPhys
(AFRI. MMSE) UBFC
2.46
3.12
4.28
0.96
Pretrain Supervised MetaPhys
(AFRI. & MMSE) / UBFC
1.90
2.62
3.84
0.96
MetaPhys (No pretrain)
(AFRL & MMSE) / UBFC
3.80
5.32
0.13
0.84
Supervised Pretrain FT on Test Support Set
(AFRL & MMSE) / UBFC
6.26
7.37
-0.23
0.72
(Unsupervised) Meta-rPPG (Lec et a (020)
Self-Collected/ UBFC
5.97
7.42
0.53
Supervised Pretrain [Liu cl al. 2020
(AFRL & MMSE) UBFC
4.42
6.13
1.87
0.79
(Unsupervised) POS Wang al. 2016
None / UBFC
6.44
9.48
0.55
0.66
(Unsupervised) CHROM (De Haan
Jeanne
2013
None UBFC
7.31
9.85
0.93
0.57
(Unsupervised) ICA (Poh al.
2010a
None UBFC
10.2
14.4
-0.19
0.50
MAB = Mean Absolute Eror, RMSE Rool Mean Squared Emor, Peanon Corvelaioon SNR = BVP Signalito Nouse Ruiio.
and used for training. Electrocardiograms (ECG) were recorded for evaluating performance. Each participant was recorded six times with increasing head motion in each task (10 degrees/second, 20 degrees/second, 30 degrees/second), The participants were asked to sit still for the first two tasks and perform three motion tasks rotating their head about the vertical axis.
UBFC (Bobbia et al., 2019): 42 videos of 42 participants were recorded at 640x480 resolution and 30 fps in uncompressed 8-bit RGB format. A CMS50E transmissive pulse oximeter was used to obtain the ground truth PPG data. All the experiments were conducted indoors with different sunlight and indoor illumination. Participants were also asked play time sensitive mathematical games to augment the heart rate during the data collection.
MMSE (Zhang al., 2016); 102 videos of 40 participants were recorded 1040x 1392 resolution and 25 fps. A blood pressure wave signal was measured at 1000 fps as the gold standard. The blood pressure wave was used as the training signal for this data as PPG signal was not available. The distribution of skin types based on the Fitzpatrick scale Fitzpatrick 1988 is: II=8, III=11, IV=17, V+VI=4.
4.2 IMPLEMENTATION DETAILS
MetaPhys implemented in PyTorch (Paszke et al. 2019), and all the experiments conducted on a Nvidia 2080Ti GPU. We first implemented the backbone network (TS-CAN) and modified it to window size of 20 frames (rather than 10) because we empirically observed larger window size led to better overall performance. Then, we implemented MetaPhys based on a gradient computation framework called higher (Grefenstette et 2019). Compared with most previous meta-learning studies that were trained and evaluated on a single dataset (e.g., minimagenet (Vinyals et al. 2016)). we used three datasets perform pretraining and cross-dataset training and evaluation. Our backbone was pretrained on the AFRL dataset, and the training (described in Algorithm 1) and evaluation of our meta- heea-eeerrer were performed with the UBFC and MMSE datasets. We picked the size of the support set (K) for personalization to be 540 video frames for each individual. For a 30 fps video recording this equates to 18-second recording which is a reasonably short calibration period. During the meta training and adaptation, used Adam optimizer (Kingma & Ba (014] with an outer learning rate (3) 0.001 and a stocastic gradient descent (SGD) optimizer with an inner learning rate (0) of 0.005. We trained the meta-learner for 10 epochs, and performed one step adaptation (i.e., gradient descent).
As baselines, we implemented traditional supervised training (TS- (TS-CAN) on AFRL and evaluated on MMSE and UBFC. A conventional fine tuning with the support set and testing the query set was implemented as our adaptation baseline. To assure a fair comparison across all experiments, forced the test data (test query set) remain the same within each task. We also implemented

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MAE UBFC and MMSE-HR Datasets
Performance by Skin Type Category MMSE-HR
Legend
Heart Rate Hean Absslute Emro (beats/min))
Heart Rate Hean Absolute Emerr (beatsimin)
16
ICA
Meta rPFC
(Lax dt 2020)
CHROM
T5-CAN-FT Set
10
5
POS
MetaPhys
TS-CAN
Pretran+Sup. MetaPhys
Unsup. MotaPhys
UBFC
HMSE HR
Dataset
Skin Type Category
Figure 2: Left) MAE in HR estimates (12- windows) for the UBFC and MMSE-HR datasets. Right) MAE in HR estimates by skin type on the MMSE-HR dataset. Standard error bars shown.
(McDuff & BBlacfford 2019). We applied post-processing to the outputs of all the methods in the same way. We first divided the remainder of the recordings for each participant into 360-frame windows (approximately 12 seconds), with no overlap, and applied a 2nd-order butterworth filter with cutoff frequency of 0.75 and 2.5 Hz (these represent a realistic range of heart rates we would expect for adults). We then computed four metries for each window: mean absolute error (MAE), root mean squared error (RMSE), bggnalto-noiss ratio (SNR) and correlation (p) in heart-rate estimations. Unlike most prior work which evaluated performance on whole videos (often 30 or 60 seconds worth of data), we perform evaluation on 12 second sequences which is considerably more challenging as the model has much less information for inference.
three established unsupervised algorithms (CHROM. POS, ICA) using
5 RESULTS AND DISCUSSION
Comparison with the State-of-the-Art: For the MMSE dataset, our proposed supervised and unsupervised MetaPhys with pretraining outperformed the state-of-the-art results by 7% and 42% in MAE, respectively (see T. On the UBFC dataset, supervised and unsupervised MetaPhys with pretraining showed even greater benefits reducing error by 57% and 44% compared to the previous state-of-the-art, respectively. Meta-learning alone is not as effective as meta-learning using weights initialized in pretraining stage (19% and 50% improvements in MMSE and UBFC). We also compared our methods against the only other meta-learning based method (Meta-PPPG) where we reduced the MAE by 68%. Furthermore, personalization method (fine-tuning), and our approach gained a 54% and a 61% % improvements in terms of MAE on MMSE and UBFC, respectively. We also evaluated data size of 6s, 12s and 18s for support set during meta training and testing, and the results showed training with 18s (RMSE: 3.12) outperformed 6s (RMSE: 5.43) and 12s (RMSE: 5.53) in the MMSE dataset. similar trend also has been observed on the UBFC dataset (RMSE of 18s: 3.12. RMSE of 12s: 4.48, RMSE of 6s: 3,46).
compared MetaPhys against the traditional
Unsupervised vs. Supervised Adaptation: Next, we examine the difference between using super- vised and unsupervised training regime in MetaPhys. For UBFC, the supervised model (MAE=1.90 BPM), outperformed the wnsupervised model (MAE=2.46 BPM). Whereas, for the MMSE dataset the unsupervised model (MAF=1.87 BMP) outperformed the supervised model (MAF=2.98 BMP). The fact that the unsupervised model achieves broadly comparable results to the supervised model surprising and encouraging because there are many applications where unsupervised adaptation would be more convenient and efficient (c.g., calibrating a heart rate measurement app on a smartphone without needing reference device). We also observe that the unsupervised model, even though it used the POS signal as training input, significantly outperforms POS on both datasets, suggesting MetaPhys is able to form better representation.
Visualizing Adaption: To help us understand why MetaPhys outperforms the state-of-the- models visualized the attention masks for different subjects. In Fig. AA compare the attention masks
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B
TS-CAN
TS-CAN+ +
Supp. Set
Protrain +
Supervised
MetaPhys
Pretrain
Unsuper.
MetaPhys
P1
P2
P3
P4
P5
Epochs
Figure 3: (A) An illustration comparing the attention masks of five subjects The masks were generated in 4 training schemes: 1) traditional supervised training (TS-CAN), 2)TS-CAN with fine tuning, 3) supervised MetaPhys and 4) unsupervised MetaPhys. (B) An illustration comparing the attention masks in the learning progress from four training schemes.
from the appearance branch of TS-CAN based on four training schemes which are: 1) supervised training with TS-CAN, 2) pretraining TS-CAN on AFRL and then fine tuning TS-CAN on the support set used for the meta-learning experiments, 3) pretraining training. 4) pretraining on AFRL and unsupervised MetaPhys training. The diffcrences are subtle, but on inspection we notice that MetaPhys leads to masks that put higher weight on regions with greater pulsatile signal (e.g., forehead and cheeks) and less weight on less important regions (c.g., t-shirt- see P5 as an example). In Fig. BB we visualize the progression of learning for the four different methods. Again the changes during learning are subtle, but the traditional supervised methods seem more likely overfit even over relatively small number of epochs meaning that the attention to important regions of the face is not as high as with the meta- meta-learning approaches, presumably because the traditional supervised learning has to capture a more generie model which is not well adapted to any one specific individual.
AFRL and supervised MetaPhys
Freezing Appearance vs. Motion: We questioned whether the adaptation of the appearance mask was the main or sole reason for the improvements provided by MetaPhys. To test this, we froze the weights in the motion branch of the TS-CAN during the meta-training stage and only updated weights in the appearance branch. From the results of these experiments, we observe that there is a 20% increase in MAE, indicating that MetaPhys not only noticeably improves the quality of the attention mask, but also learns additional temporal dynamics specific to individual's 's pulse waveform.
Our motivation for adopting a meta-learning approach is to improve generalization. One challenge with photoplethyysmorrphy methods is their sensitivity to skin type. A larger melanin concentration in people with darker skin leads to higher light absorption compared to lighter skin types (Nowara al. 2020)), thus reducing the reflectance signal noise ratio. Fig. shows a bar plot of the MAE in heart rate estimates by skin type (we group types I+II and V+VI as there were relatively few subjects in these categories). Both the AFRL and UBFC datasets are heavily skewed towards lighter Caucasian skin type categories. Therefore supervised methods trained on these datasets (e.g.. TS-CAN) tend to overfit and not perform well on other skin types. Entirely unsupervised baselines do not perform any better, possibly because they were mostly designed and validated with lighter skin type data as well. While the highest errors for unsupervised MetaPhys still come in the darkest skin type categories, the reduction in error for types V+VI is considerable (68% compared to POS, 50% compared to TS-CAN), We are encouraged that these results are a step towards more consistent performance across people of different appearances.
Robustness to Skin Type:
Limitations: There is a trend of performance degradation when the skin type gets darker. We acknowledge this limitation and plan
resampling to help address this bias in future. Both the MMSE and UBFC datasets have somewhat limited head motion and future work will investigate whether meta-learning can help with generalization other motion conditions.
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6 CONCLUSIONS
We present a novel unsupervised few-shot adaptation framework for non-contact physiological measurement called MetaPhys. Our proposed method substantially improves on the state-of-the-ae and the performance on various skin types, and we also reveal why and how our method achieved such improvement.
REFERENCES
Freddy Abnousi, Guson Kang, John Giacomini, Alan Yeung, Shirin Zarafshar, Nicholas Vesom, Euan Ashley, Robert Harrington, and Celina Yong. A novel noninvasive method for remote heart failure monitoring: the eulerian video magnification applications in heart failure study (amplify). NPJ digital medicine, 2(1):1-6, 2019.
Guha Balakrishnan, Fredo Durand, and John Guttag. Detecting pulse from head motions in video. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3430-3437, 2013.
Nannapas Banluesombatkul, Pichayoot Ouppaphan, Pitshaporn Leelaarporn, Payongkit Lakhan, Busarakum Chaitusaney, Nattapong Jaimchariyatam, Ekapol Chuangsuwanich, Wei Chen, Huy Phan, Nat Dilokthanakul, et al. Metasleeplearner: A pilot study on fast adaptation of bio-signals- based sleep stage classifier to new individual subject using meta-learning. 2020.
Serge Bobbia, Richard Macwan, Yannick Benezeth, Alamin Mansouri, and Julien Dubois. Unsu- pervised skin tissue segmentation for remote photoplethysmography Pattern Recognition Letters, 124:82-900 2019.
Joy Buolamwini and Timnit Gebru. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency, pp. 77-91, 2018.
Weixuan Chen and Daniel McDuff. Deepphys: Video-based physiological measurement using convolutional attention networks. Proceedings of the European Conference on Computer Vision (ECCV), pp. 349-365, 2018
Janghoon Choi, Junseok Kwon, and Kyoung Mu Lee. Deep meta learning for real-time target-aware visual tracking. In Proceedings of the IEEE International Conference on Computer Vision, pp. 911-920, 2019.
Gerard De Haan and Vincent Jeanne. Robust pulse rate from chrominance-based rppg. IEEE Transactions on Biomedical Engineering, 60(10))2878-25)) 2013.
Justin R Estepp, Ethan Blackford, and Christopher M Meier. Recovering pulse rate during motion
artifact with a multi-imager array for non-contact imaging photoplethysmography. In 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1462-1469. IEEE, 2014.
Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the 34th International Conference Machine Learning- Volume 70, pp. 1135-1 2017.
Thomas Fitzpatrick. The validity and practicality sun-reactive skin types i through vi. Archives of dermatology, 124(6):869-8) 871, 1988.
Taesik Gong, Yconsu Kim, Jinwoo Shin, and Sung-Ju Lee. Metasense: few-shot adaptation to untrained conditions deep mobile sensing. In Proceedings of the 17th Conference on Embedded Networked Sensor Systems, pp. 110-123, 2019.
Edward Grefenstette, Brandon Amos, Denis Yarats, Phu Mon Htut, Artem Molchanov, Franziska Meier, Douwe Kicla, Kyunghyun Cho, and Soumith Chintala. Generalized inner loop meta-lcarning. arXiv preprint arXiv: /010:///100//2 2019.
Junfeng He, Khoi Pham, Nachiappan Valliappan, Pingmei Xu, Chase Roberts, Dmitry Lagun, and Vidhya Navalpakkam. On-device few-shot personalization for real-time gaze estimation. In Proceedings of the IEEE International Conference Computer Vision Workshops, pp. 0-0, 2019.

---

Under review as a conference paper at ICLR 2021
Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey. Meta-learning in neural networks: survey, arXiv preprint arXiv:2004.05439, 2020.
Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint 1412.6:412.69860 2014.
Eugene Lee, Evan Chen, and Chen- Lee. Meta-rppg: Remote heart rate estimation using a transductive meta-learner. Proceedings of the European Conference Computer Vision (ECCV), 2020.
Jessica Lee, Deva Ramanan, and Rohit Girdhar. Metapix: Few-shot video retargeting. arXiv preprint 1rX1v:1910.04442, 2019.
Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li. Meta-sgd: Learning to learn quickly for few-shot learning, arXiv preprint urXiv:1707.0900 2017.
Lin, Chuang Gan, and Song Han. Tsm: Temporal shift module for efficient video understanding. In Proceedings of the IEEE International Conference Computer Vision, pp. 7083-7093, 2019.
Xin Liu, Josh Fromm, Shwetak Patel, and Daniel McDuff. Multi-task temporal shift attention networks for on-device contactless vitals measurement. arXiv preprint arXiv:2006.03799) 2020.
Andrea Madotto, Zhaojiang Lin, Chien-Sheng Wu, and Pascale Fung. Personalizing dialogue agents via meta- learnng. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5454-5459, 2019.
Daniel McDuff and Ethan Blackford. iphys: An open non-contact imaging-based physiological measurement toolbox. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6521-6522 IEBE, 2019.
Ewa M Nowara, Daniel McDuff, and Ashok Veeraraghavan. A meta-analysis of the impact of skin tone and gender on non-contact photoplethysmograhh measurements. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 284-285, 2020.
Eunbyung Park and Alexander Berg. Meta-tracked Fast and robust online adaptation for visual object trackers. In Proceedings of the European Conference Computer Vision (ECCV), pp. 569-585, 2018.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. In Advances in neural information processing systems, pp. 8026-8031 2019.
Ming-Zher Poh, Daniel J McDuff, and Rosalind W Picard. Advancements in noncontact, multiparam- eter physiological measurements using a webcam, IEEE transactions on biomedical engineering. 58(1))7-11, 2010a. 2010a.
Ming- -Zer Poh, Daniel J McDuff, and Rosalind W Picard. Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Optics express, 18(10):10762. 10774, 2010b.
Valentina Puntmann, Ludovica Carerj, Imke Wieters, Masia Fahim, Christophe Arendt, Jedrzej Hoffmann, Anastasia Shchendrygina, Felicitas Escher, Mariuca Vasa-Nicotera, Andreas M Zeiher, et al. Outcomes of cardiovascular magnetic resonance imaging in patients recently recovered from coronavirus discase 2019 (covid-19). JAMA cardiology, 2020.
Anthony C Smith, Emma Thomas, Centaine L Snoswell, Helen Haydon, Atcev Mchrotra, Jane Clemensen, and Liam J Caffery. Telehealth for global emergencies: Implications for coronavirus disease 2019 (covid-19) 19). Journal of telemedicine and telecare, pp. 1357633X20916567, 2020.
Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. In Advances in neural information processing systems, pp. 4077-4087, 2017.
10

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Under review as a conference paper at ICLR 2021
Chihiro Takano and Yuji Ohta. Heart rate measurement based on time-lapse image. Medical engineering & physics, 29(8):853-88) 2007.
Wim Verknuysse, Lars O Svaasand, and Stuart Nelson. Remote plethysmographic imaging using ambient light. Optics express, (6626):21434-21444 2008.
Mauricio Villarroel, Sitthichok Chaichulee, João Jorge, Sara Davis, Gabrielle Green, Carlos Arteta, Andrew Zisserman, Kenny McCormick, Peter Watkinson, and Lionel Tarassenko. Non-contact physiological monitoring of preterm infants in the neonatal intensive care unit. npj Digital Medicine, 2(1):1-18, 2019.
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al. Matching networks for one shot learning. Advances in neural information processing systems, pp. 3630-3638, 2016.
Wenjin Wang, Albertus den Brinker, Sander Stuijk, and Gerard de Haan. Algorithmic principles of remote ppg. IEEE Transactions on Biomedical Engineering, 64(7):1479-1491, 2016.
Hao- Yu Wu, Michael Rubinstein, Eugene Shih, John Guttag, Frédo Durand, and William Freeman. Eulerian video magnification for revealing subtle changes in the world. ACM transactions graphics (TOG), -4) 2012.
Zitong Yu, Xiaobai Li, and Guoying Zhao. Remote photoplethysmooraphh signal measurement from facial videos using spatio-temporal networks. In Proc. BMVC, pp. 1-12, 2019.
Qi Zhan, Wenjin Wang, and Gerard de Haan. Analysis of enn-based remote-ppe ppg to understand limitations and sensitivities. Biomedical Optics Express, 1 1163):1268-1283 2020.
Zheng Zhang, Jeff M Girard, Yue Wu, Xing Zhang, Peng Liu, Umur Ciftci, Shaun Canavan, Michael Reale, Andy Horowitz, Huiyuan Yang, al. Multimodal spontaneous emotion corpus for human behavior analysis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3438-3446, 2016.
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le. Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8697-8710 2018.
A APPENDIX
A. EVALUATION METRICS
Mean Absolute Error (MAE): The MAE between our model estimates and the gold-standard heart rates from the contact sensor measurements were calculated as follows for each 12 12-second time window:
MAE Σ HR, HR
Root Mean Squared Error (RMSE): The RMSE between our model estimates and the gold- standard heart rate from the contact sensor measurements were calculatd as follows for each 12-second time window:
RMSE
HR, HR)
(2)
T
In both cases, HR is the cold-standard heart rate and HR' is the estimated heart rate from the video respectively. The goad-standard HR frequency was determined from the calculated from gold-standard PPG signal (UBFC dataset) or blood pressure wave (MMSE dataset).
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Under review as a conference paper at ICLR 2021
We also compute the Pearson correlation between the estimated heart rates and the gold-standard heart rates from the contact sensor measurements across all the subjects.
Signal-to-Noise Ratios (SNR): We calculate blood volume pulse signal-to-noise ratios (SNR) (De Haan & Jeanne 2013). This captures the signal quality of the recovered pulse esti- mates without penalizing heart rate estimates that are slightly inaccurate. The gold-standard HR frequency was determined from the gold-standard PPG waveform (UBFC dataset) or blood pressure wave (MMSE dataset).
240
SNR = 10log10
(3)
30= ((()))(())))
240
where Š is the power spectrum of the BVP signal (S), the frequency (in BPM), HR is the heart rate computed from the gold-standard device and U.(f) is binary template that is for the heart rate region from 6 BPM to HR+6BPM and its first harmonic region from 2* 11BMM to and 0 elsewhere.
A.2 CODE AND ADDITIONAL RESULTS:
Available at: /etpss//giie com/ ano.yyoouusspppeee///ee2aaaaa
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Reviews:
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The motivation is that traditional fine-tuning method for this task is difficult since it requires large sets of high-quality training data for specific individuals, due to differences between each individual, measurement environment, and camera sensor condition. Therefore, the authors applied MAML on top of the existing deep learning network (TS-CAN) and implemented a model that aims to learn fast from a small number of training samples. The main contributions of this paper are: a meta-learning model that supports both supervised and unsupervised few-shot adaptation; improved performance by about 40% compared to a baseline that does not use meta learning; empirical analysis of performance for subjects with different skin types. \n\nStrengths- \nFew-shot learning could be beneficial for remote patient monitoring applications.  \nExperimental results on all three datasets are positive, with around 40%-50% MAE improvement compared to the baseline.  \nProvided experimental evaluation provides some insight into why the proposed approach works well in this application.  \n\nWeaknesses-  \nThis is a straightforward application of meta learning / few shot learning to a specific domain. Other than showing that meta learning / few shot learning is useful in yet another particular application, it is not clear how is this work advancing machine learning. \nSome explanations could have been more specific. For example, it is not clear how exactly are ‘pseudo labels’ generated and how do they compare with the ground truth labels.  \nThe experimental results do not do enough to provide some novel insight into MAML. For example, it would be good to see a comparison between pretrained model and MAML model performance as a function of data size, and how robust to hyperparameters are the competing approaches.   \n\nSummary of the rating:\nWhile the experimental results are positive, there is little methodological novelty and the experimental results do not seem to provide some interesting insights that might spur machine learning research in some novel directions.", "extra_scores": {}, "extra_text": {}}, {"review_id": "XtkwGiedq6", "reviewer": "AnonReviewer4", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "Summary:\nThe paper proposes an (un)supervised meta-learning framework for few-shot adaptation in camera-based vital sign measurements denoted as \"MetaPhys\". The goal is to develop few-shot adapting personalized frameworks for remote physiological measurements with limited data. Authors perform cross-dataset evaluations to illustrate the model's few-shot adaptation capabilities.\n\nPositives:\nThe authors present their application domain and purpose very clearly, and the related work from literature are nicely explored. Experimental evaluations are thorough and the comparisons to state-of-the-art unsupervised approaches are done well.\n\nConcerns:\nUnfortunately I do not see any methodological contribution in this paper from a \"representation learning\" perspective. MetaPhys is (as far as it is presented in the paper) a framework on using model-agnostic meta-learning (MAML) on physiological measurement data in unsupervised subject adaptation settings. Since MAML is already a model-agnostic approach, the paper seems to me like a well-presented reformulation and implementation of a well-known meta-learning algorithm in this respective application domain. The paper does not make a clearly stated distinction of their approach/contribution from a representation learning standpoint.\nThe authors list their contributions in four bullet-points, where the third and fourth ones relate to analyses of temporal and appearance features during model personalization, and analyses for subjects with different skin types. However these analyses are only addressed superficially (not described or analyzed in detail) at the end of the paper. Hence I would be questioning these as \"contributions\" of this paper.\n\nMinor comment:\nIn fact, the authors present MetaPhys for both supervised and unsupervised few-shot adaptation settings. Then why did the authors use \"unsupervised\" indication for the paper's title (and even in Abstract)?\n", "extra_scores": {}, "extra_text": {}}, {"review_id": "SsCrxJpaKiO", "reviewer": "AnonReviewer5", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The authors propose a system called MetaPhys for personalized remote physiological sensing from videos.  Their system combines a pre-trained CNN with an existing meta learning method (MAML). The investigated both supervised and unsupervised training of their system.  Performance evaluation of their methods on benchmark datasets show their model significantly outperforms SOTA methods using multiple metrics as well as for different skin types. They further show that the unsupervised model achieves comparable results to the supervised model.\n\nThis is an interesting paper, with extensive evaluation that demonstrates their model’s superior performance  to SOTA methods. The technical novelties of their model, however, are incremental since it is primarily built the existing models : a pre-trained CNN, an existing meta-learning method (MAML), and use of the POS method to generate pseudo-labels for unsupervised learning.  \n\n", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This application-oriented paper has been carefully evaluated by three expert reviewers. Their assessments all agreed on a quite marginal methodological novelty of the presented work, yet they recognized nicely engineered pipeline of pre-existing modules that appears to satisfy an important remote sensing application. I agree with that assessment and concur with the reviewer's opinion that the work as presented, in spite of being of potential interest to healthcare or biomedical communities, will be of little interest to the ICLR audience. Therefore I recommend rejecting it.  
Decision: Reject