RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data
Authors: Maxwell A Xu
Jaya Narain
Gregory Darnell
Haraldur T Hallgrimsson
Hyewon Jeong
Darren Forde
Richard Andres Fineman
Karthik Jayaraman Raghuram
James Matthew Rehg
Shirley You Ren
Venue: ICLR 2025 Poster (2025)
Keywords: imu
har
biosignals
activity classification
gait metrics
time-series
foundation model
contrastive learning
self-supervised learning
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: We present RelCon, a novel self-supervised Relative Contrastive learning approach that uses a learnable distance measure in combination with a softened contrastive loss for training an motion foundation model from wearable sensors.

Abstract: We present RelCon, a novel self-supervised Relative Contrastive learning approach for training a motion foundation model from wearable accelerometry sensors. First, a learnable distance measure is trained to capture motif similarity and domain-specific semantic information such as rotation invariance. Then, the learned distance provides a measurement of semantic similarity between a pair of accelerometry time-series, which we use to train our foundation model to model relative relationships across time and across subjects. The foundation model is trained on 1 billion segments from 87,376 participants, and achieves strong performance across multiple downstream tasks, including human activity recognition and gait metric regression. To our knowledge, we are the first to show the generalizability of a foundation model with motion data from wearables across distinct evaluation tasks.

Published as a conference paper at ICLR 2025
RELCON: RELATIVE CONTRASTIVE LEARNING FOR A MOTION FOUNDATION MODEL FOR WEARABLE DATA
Maxwell A. Xu Jaya Narain', Gregory Darnell', Haraldur Hallgrimsson' Hyewon Jeong!.
Darren Forde', Richard Fineman', Karthik J. Raghuram', James M. Rehg'. Shirley Ren!
Apple Inc. PIUC, MITT
edu, jnarain@apple.com
ABSTRACT
We present RelCon, a novel self-supervised Relative Contrastive learning ap- proach for training motion foundation model from wearable accelerometry sen- sors". First, a learnable distance measure is trained to capture motif similarity and domain-specifie semantic information such as rotation invariance. Then, the learned distance provides a measurement of semantic similarity between a pair of accelerometry time-series, which we use train our foundation model model relative relationships across time and across subjects. The foundation model is trained on billion segments from 87,376 participants, and achieves state-of- the-art performance across multiple downstream tasks, including human activity recognition and gait metric regression. To our knowledge, we are the first to show the generalizability of foundation model with motion data from wearables across distinct evaluation tasks.
INTRODUCTION
Advances in self-supervised learning (SSL) combined with the availability of large-scale datasets have resulted in a proliferation of foundation models (FMs) in computer vision (Oquab al., 2023), language (OpenAI et al., 2023), and speech understanding (Yang et al., 2024). FMs provide pow- erful, general-purpose representations for particular domain of data, and support generalization to a broad set of downstream tasks without fine-tuning, In contrast, health times-series have not yet benefited from the foundation model approach, with few exceptions (Abbaspourazad al., 2024; Pillai et al., 2024; McKeen et al., 2024). This is particularly unfortunate for problems in mobile health (mHealth) signal analysis, which encompasses data modalities such as IMU, PPG, and ECG, as the collection of mHealth data is both time-consuming and expensive (Rehg et al., 2017). How- ever, recent advances in sulf-supervised learning for health time-series (Liu et al., 2023) have shown promise, raising the question of whether it is now feasible to train mHealth foundation models.
In this paper, we demonstrate, for the first time, the feasibility of developing a foundation model for the analysis of accelerometry data across multiple tasks. Accelerometry an important mHealth sensor used in human activity recognition (HAR) (Fu et al., 2020), physical health status assess- ment (Xu et al., 2022), energy expenditure estimation (Stutz et al., 2024), and gait assessment (Ap- ple, 2021), among many other tasks. We use novel method for self-supervised learning combined with pre-training on large-scale, longitudinal accelerometry dataset, the Apple Heart and Move- ment Study (MacRae, 2021). We show that this approach yields an effective representation for accelerometry, useful for multiple downstream tasks, including HAR and gait metric estimation.
The key to our approach is a novel method for self-supervised contrastive learning, RelCon. Our approach exploits the observation that many time-series of interest are typically composed of motifs, short distinctive temporal shapes that may approximately repeat themselves over time (Mueen et al., 2009). An example is the repeating upward swing of the arm while walking, captured by a wrist- worn accelerometer. We learn a motif-based distance function and use this distance to identify positive and negative pairs sampled from within- and between-person time series for contrastive
Work done while at Apple Coode implementation can he found https: http:t/p///ggtih....///aa//////e httptchub..ccmm/axxo////rreelll

---

Published as a conference paper at ICLR 2025
(a) RelCon
(b) REBAR
Workout Class
Kickboxing
FuncStrength
Pilates
Yoga
Swimming
Rowing
Climbing
(c) AugPred
(d) SimCLR
StairClimbing
Elliptical
Hiking
OutdoorWalling
IndoorWalking
OutdoorRunning
IndoorRunning
OutdoorCycling
IndoorCycling
Figure 1:
t-SNE of representation spaces with perplexity=100. Our RelCon approach has the clearest clusterings based on semantic classes, even forming a specific swimming cluster that is not seen in the other methods. RelCon can also better clearly separate between In/Outdoor Cycling.
learning (Xu et al., 2024). The resulting embedding space captures the natural temporal evolution in physiological states within-person (i.e. fatigue over time) (Glass & Kaplan, 1993), and unique differences specific physiologies between-persons (Kuncan et al., 2022; Pfeifer & Lohweg. 2018).
In this work, we introduce a novel relative contrastive loss function that models the degree of "pos- itiveness" between the anchor and each candidate, preserving the relative relationships between instances. This helps capture diverse degrees of semantic similarity, reflecting the natural relation- ships found in the real-world where motions are often similar but never identical. For example, given an anchor signal of "walking," another "walking" signal should be closest, followed by "running," then "cycling," " and "yoga" as the most distant. Additionally, we introduce accelerometry -speciff augmentations so that our distance measure can learn to compare motifs, while being invariant to the sensor's s position and orientation. Our results show that RelCon can capture subtle semantic dif- ferences between similar classes (i.e. indoor vs. outdoor cycling), as well as capture the temporally- precise information necessary for gait regression tasks. This is illustrated Fig. 1, which shows that semantically related classes cluster more effectively in the RelCon feature representation.
The public code repository with RelCon training set-up, architecture, and evaluation can be found here: https:///ithh:
cwm http//aaxxu0/e/ee mrxxuu5/reeco.... Our key contributions are: 1. We introduce RelCon, novel contrastive learning methodology that models relative differences between instances via a learnable motif-based distance measure. This aims to model the varying degrees of semantic similarity found in real-world motion data.
We utilize RelCon to pre-train a foundation model on lb accelcrometry segments across 87k participants from the Apple Heart and Movement Study. This large-scale, longitudinal dataset, sourced from real-world wearable data, enables modeling of human motion dynamics across diverse contexts, supporting robust applications in health and activity monitoring.
Our RelCon-trained motion foundation model achieves state-of-the performance against 11 models across unique downstream datasets with different tasks, ranging from classification of 16 in-the-wild activities to regression temporally-precho gait metrics. We are the first to show the generalizability of motion foundation model across distinct, diverse evaluations.
RELATED WORK
Motion time-series FM: We believe the first to train foundation model for accelerom- etry data. We define foundation models to be representations that are pre-trained on broad-scale data and are capable of solving multiple diverse downstream tasks without fine-tuning (Bommasani
2

---

Published as a conference paper at ICLR 2025
RelCon Joum)
du atu
AugPred alanstaa,,
SIMCLR (brgget at, 2000)
Intencttore
Models
V
Bcteemn-Ue
Resissant
V
Polse
Powllives
Palse
Negatives
Then apply Meatie Chetrratne
Ancher
Survel Cantilltee
Siow Frck
Andiv
Then antren
Diagrams
sraly
Sean 2r1
Andiue
Fermar
Scane
Porrurad?
Figure 2: SOTA Accelerometry SSL Methods. Each sequence color represents different user's time-series. RelCon draws candidates from both within- and between-user and ranks them by their relative similarity via a learnable distance function. Then, it iteratively applies a contrastive loss, selecting one candidate as positive while assigning the more distant as negative. This helps prevent false positives/negatives because the full relative ranking is captured. Prior approaches define single positive/negative set, risking semantic errors if positive/negatives are misdefined. AugPred and SimCLR are resistant to false positives because they construct positive pairs via semantics- preserving augmentations, but REBAR does not have semantically-ccostrranee pair construction.
al., 2021) (e.g., each task uses frozen weights with supervised training of a light-weight predic- tion head). The closest related work is Yuan et al. (2024), which uses 700k person-days of data from UK Biobank to train an accelerometry representation. While their model is evaluated on multiple datasets, all of the tasks are HAR, making it unclear whether the learned representation captures broader motion information. In contrast, we test our FM on multiple gait analysis tasks as well as HAR, demonstrating the effectiveness of our representation for both regression and classification tasks. Narayanswamy et al. (2025) also focuses on HAR but uses minute-level hand-crafted features instead of the raw 100 Hz signals necessary for temporally --reccspeeeeeee motion tasks, such fall detec- tion (Sucerquia et al., 2018) or gait assessment (Alobaidi et al., 2022). Other notable FMs focus on general time-series forecasting (Das al., 2023; Ansari et al., 2024) do model accelerometry motion data (Goswami et al., 2024; Abbaspourazad et al., 2024: Saha et al., 2025; Song et al., 2024).
Our RelCon architecture introduces novel contrastive learning approach for time-series data. It is most closely-relatd to REBAR's (Xu et al., 2024) motif-based positive pair generation approach. However, REBAR is not adapted accelerometry and lacks invariance to well-understood accelerometry-semmanne sesmaanti--peeervinng transformations. Additionally, REBAR em- ploys a "hard" contrastive loss, leading sensitivity to false positives and negatives due to rigidly designating one positive and all others negatives. RelCon overcomes these limitations by learn- ing motif-based distance with accelerometry-speecfii augmentations and introducing a relative loss function to mitigate false labeling effects. Most prior motion SSL approaches focus exclusively on HAR (Logacjov, 2024; Haresamudram et al., 2024; Straczkiewicz et al., 2021). Haresamudram al. (2022) benchmarked number of SSL. approaches accelerometer data, testing generalizabil- ity across datasets and sensor positions. The best methods focus on either utilizing augmentations SinCLR-fashion (Tang al., 2020) or utilizing them as prediction targets (Saeed al., 2019). Comparison of these two models to REBAR can be seen in Fig. 2. Models for other motion-based Supics-include walking speed (Shrestha & Won, 2018; Soltani et al., 2021), gait (Slemenšek al., 2023; Brand et al., 2024), and health monitoring (Takallou et al., 2024) have focused on single-task supervised learning. For alternative biosignals, such as PPG (Pillai al., 2024), ECG (Jeong al., 2023; Diamant et al., 2022), and EEG (Pradeepkumar et al., 2025), there have been recent works that develop domain-specifie SSI approaches.
Motion time-series SSL:
Soft Label Learning Approaches: A feature of our RelCon approach is the use of soft labels to obtain more fine-grained characterizations of similarity. Most prior self-supervised contrastive methods for time-series have adopted the hard label approach (Yue et al., 2022; Tonekaboni et al., 2021: Zhang et al., 2022), including the previous REBAR method (Xu et al., 2024). One exception is Lee et al. (2023), which proposed soft contrastive approach for classification and anomaly detection. However, unlike RelCon, they utilize non-leaeeblle distance measures, such as dynamic time warping (Müller, 2007), that cannot be trained to capture domain-specific semantic features.
3

---

Published as a conference paper at ICLR 2025
3 METHODOLOGY
The RelCon methodology has two key components. The first in its innovations to learn distance measure that captures semantic accelcrometry information, described in Section 3.1. The second is a novel relative contrastive loss that encodes relative order relationships, described in Section 3.2.
3.1 LEARNABLE DISTANCE MEASURE
Following prior work (Xu et al., 2024), train neural network to learn distance measure to identify whether two sequences have similar temporal motifs (Schäfer & Leser, 2022) and are se- mantically similar. After training, the architecture is frozen and used static function to determine the relative similarities of candidate samples in the RelCon approach. The distance measure archi- tecture is defined in Eq. below:
d(Xancs Xeand)
X
anc/cand Xanc
anc jeand
= ((CrossAttm X Xcand W. + bo) + Meand Frand
(2)
CrossAttn Xane X
Σ
sparsemax (sim| (fg (Xanc), fu (Xcand) (Neand)
(3)
Neaue Exand
Neasc Xxand
[a/k//o) (X (ane/cand))
DilatedConvNet
(anz/con)) Meard
(4)
dessd
where X E RRDD and J4, E TRD with 7 as the time length and D=3 for our 3-axis accelerometry signals. The distance between an anchor sequence and candidate sequence, d(X Xeana), is defined as the reconstruction accuracy to generate the anchor from the candidate. The distance measure is strictly dependent the motif similarities between the anchor and candidate that are captured the dilated convolutions in f(//k) (Xu et al., 2024). See code for further details.
Prior work with this distance measure only captured simple exact-motif-matte matching mechanism because the original masked reconstruction training task can be solved by exact-matching the non- masked regions. To enhance the distance measure, we introduce key innovations:
1. Use Accel-specific augmentations (Tang et al., 2020) during training to learn mechanism that invariant to
transformations.
During training. we define Xend == AAgg(anc) making the candidate an augmented version of the original sequence, using augmentation procedure from Accel SimCLR (Tang et al., 2020). This prevents exact-match solution and helps the model learn to reconstruct the original anchor from semantically similar but altered candidates, such as recognizing running signals despite variations like upside-down wearable or noise from foose-fitting device.
Replace the softmax in the cross-attention with a sparsemax formulation (Martins & Astudillo, 2016) in Eq. 3 to encourage precise motif comparison.
Sparsemax returns the euclidean projection of the unnormalized logits onto the probability simplex, encouraging sparsity (Martins Astudillo, 2016). This prevents diffuse attention distributions that compare minor, irrelevant motifs within the anchor and candidate. Sparsemax ensures the model reconstructs with distinct features, enabling our measure to capture class slecifspecific information.
3. Use reversible instance normalization (Kim et al., 2021) to normalize an anchor based upon the candidate, preserving relative manhor-aandidate magnitude information.
The anchor sequence and final reconstruction output are normalized (in Eq. 4) and unnormalized (in Eq. 2) using candidate sequence statistics. When an anchor and candidate have drastically different magnitudes, it is more likely they represent different fundamental motions and should have increased reconstruction error. Reversible normalization helps preserve this effect.
3.2 RELATIVE CONTRASTIVE LoSS: ALL PAIRS ARE POSITIVE ... BUT SOME PAIRS ARE MORE POSITIVE THAAN OTHERS
Normalized temperature cross entropy loss (NT-Xent) is standard in contrastive learning (Eq. 5). learns a class discriminative embedding space by pulling the anchor and positive instances closer together in the embedding space and pushing all negatives away from the anchor, as in:
exp( (ismmmmx.,,p (Xanc: Xpm.)/T)
8(Xanc: Xpos: Sneg) =llog
Σx ExH ES exp(sim(2 //r + exp(ssi(Xnmo) Xpos)/T) (5)
4

---

Published as a conference paper at ICLR 2025
where sim(Xanc Xpos) = (E E(Xpes) po and E(-) is some encoder function. Traditionally, NT- Xent uses strict, "hard" labels that define absolute positive and negative sets. However, we would like to use the learned distance measure to capture relative ordering among the candidates in the loss. That is, if d((Xxee: X;) > d(Xame:X) Xj), the loss learns to preserve that relative ordering in the Fl(-'ss embedding space. To achieve this, redefine Sny Sney(X (Xame: X,, Scand) function in Eq. 6, allowing the set of negatives to dynamically change based on the current positive pair. This enables the construction of our Relative Contrastive Loss function Eq. 7:
Ieeg (uancy Xpus S) = (X E S d(xime; X) d(Xane, xpes)}
(6)
CReeCon
Σ
E(Xame,
Xpos == Xi. Sneg == Sneg (Xane, X1, Seand))
XiEScad
This loss iteratively calculates NT-Xent over each candidate, X; E Scand. iteratively setting each can- didate as the positive, Xpos X, The negative set for the given positive is then defined all candi- dates with a greater distance measure than the given positive {X E S: d(Xanc. X) > d(Xanc+ Xpos) }. Candidates with larger distances from the anchor appear in more negative sets, reinforcing the rela- tive ranking. In this way, the RelCon loss function captures hierarchical structure of similarities in the embedding space based on their relative relationships with the anchor.
In order to capture diverse relationships via RelCon, our pool of candidates originates from two sources: (1) sampling within the user, across time and (2) sampling between users, within the batch. In (7), we choose random subsequences from the same user as the anchor sequence. This helps capture within-person temporal dynamics in the encoder, such as how a user's motion signals may indicate fatigue over time. In (2), sampling between- uuveer allows the model learn physiological similarities and differences across other users. Our RelCon procedure is visualized in Fig. 2.
4 EXPERIMENTAL DESIGN
4.1 FOUNDATION MODEL PRETRAINING
We trained models Inertial Movement Unit (IMU) sensor data from the Apple Heart & Movement Study (AHMS) (MacRae, 2021). AHMS is an ongoing research study designed to explore the links between physical activity and cardiovascular health, which is sponsored by Apple and conducted in partnership with the American Heart Association and Brigham and Women's 's Hospital. To be eligible, participants must among other eligibility criteria be at least 18 years of age (at least 19 in Alabama and Nebraska; at least 21 in Puerto Rico), reside in the United States, have access to an Apple Watch, and provide informed consent electronically in the Apple Research app.
The training data included a subset of study data with 87,376 participants recorded over one day. with a 10/3/3 train/val/test split. Following prior convention (Reyes-Ortis Ortizz et al., 2015), we use 2.56 seconds of the raw 100 Hz 3-axis x.y.z accelerometry signal of the IMU sensor in the wearable device as input to our embedding model. The model was pre-trained with 8 x A100 GPUs for 24 hours. It iterated over total of 1 billion unique samples, i.e. ~30k unique participant-daa used a ID ResNet-34 encoder backbone (3.9M parameters) with a final global average pooling to generate 256-dimensional embedding vector.
We
4.2 DOWNSTREAM EVALUATION
We group our downstream evaluations into two sections: Task Diversity and Benchmarking, In total, we compare against 6 different downstream datasets across different types of downstream tasks. We compare our RelCon foundation model against 11 models total: 3 pre-trained from scratch in the Task Diversity Evaluation and from the prior literature in the Benchmarking Evaluation.
In Task Diversity Evaluation, we evaluate how well our foundation model's learned embedding generalizes across diverse downstream tasks, in order to understand how SSL via RelCon enables broad applicability. To this end, these downstream tasks include gait metric regression for predicting stride velocity and double support time and human activity recognition for classifying in-the-wild ac- tivities the subsequence-leve and on the workout-level. These tasks are two separate datasets. Then, we compare our RelCon -oroiieeeeedd foundation model against 3 different models, each praitrained from seratch with different SOTA SSL methodology under the same, controlled conditions.
5

---

Published as a conference paper at ICLR 2025
In Benchmarking Evaluation, compare our foundation model against prior works on standard- ized, collicly-available human activity recognition benchmarks. Specifically, we compare against Large-Scale Pre-trained model (Yuan et al., 2024) and against Accel SSL Benchmarking Study (Haresamudram et al., 2022) with 7 distinct SSL methods. These datasets evaluate generalizability under data distribution shifts, including differing sensor positions and inference window lengths.
4.2.1 TASK DIVERSITY EVALUATION SET-UP
Following the same exact training conditions as our RelCon foundation model (i.e. AHMS dataset, total training iterations, encoder backbone), we pre-trained 3 other state-of-the- art accelerometry SSL methods from scratch, which can seen below. Visualizations of how each method works can be found in Fig. 2. Further implementation details in Appendix A.2.
Baselines:
Accel SimCLR (Tang et al., 2020) contrasts positive pairs formed by accel-specific augmentations (i.e. 3D rotation) and demonstrates state-of-the oftt--oftth-arr art performance (Haresamudram et al., 2022). Augmentation Prediction (Saeed et al., 2019) predicts whether or not a specific augmentation was applied. This is classic accelerometry approach that demonstrates competitive performance (Haresamudram et al., 2022) with good scaling ability (Yuan et al., 2024).
Accel REBAR (Xur et al., 2024) uses a "hard" contrastive loss with learned
to iden- tify positive pairs, relying on fixed positive and negative sets without capturing relative positions. For a fairer comparison, we adapt REBAR with the accel-specific enhancements from Section 3.1.
All participants completed proctored overground walking tasks with two mobile devices with IMU sensors at each side of the body in different locations (i.e. at the hip, in a front/back pocket, or waist bag) (Apple, 2021). We used the Cohort participants, which included 359 participants with an average age of 74.7, who provided informed consent. Participants conducted 3 walking tasks: 1) one lap at self-selected speed, 2) four laps at an instructed slow speed, and 3) as many laps as possible within six minutes. Each task was conducted along straight 12- meter course, with an 8-meter pressure mat placed centrally. Various gait statistics were calculated, such double support time and stride velocity, which our models were evaluated on.
Gait Metric Regression:
Each 2.56s subsequence was matched to the lap aggregated target (e.g. total number of steps or average walking speed). The participants were assigned into 50% train and 50% test randomly based on participant ID, where every lap for a given participant falls into the same split. The training split was used to train linear regression probes embeddings from the self-supervised models. Metrics were selected following a prior report (Apple, 2021): mean squared error (MSE), std dev of squared error (SDSE), mean absolute error (MAE), std dev of absolute error (SDAE), and Pearson Correlation Coefficient. Mean and standard deviations were calculated by aggregating predictions across all errors of given user and are related to bias and variance. Correlation is used assess how each user's s average gait metric corresponds to ground truth values. Ranges per metric are obtained by retraining the probe five times and calculating the mean and standard deviation.
Field Human Activity Recognition: We evaluated activity classification using self- reff-reported activity labels gathered from in-the-wild, unproctored, field data in AHMS. We used a subset of data with ~2 total users across 16 workouts (full list in Fig. 1). These workouts captured a range of diverse activities (e.g. kickboxing and rowing) that are non-trivial to separate (i.e. outdoor cycling vs. indoor cycling). For evaluation, workouts were class balanced with each including ~22 hours of data, for a total of 310 workout hours. We used a 4/1/5 train/val/test split based on participant ID. We ensured this data did not overlap with the pre-training dataset, preventing any data leakage.
Embeddings are generated for each 2.56s-long subsequence. We evaluate classification on both the subsequence-level as well as the workout-level. At the workout-level, we predict activity across each of 2.56s subsequences within the workout, and select the most frequently predicted activity. Including two temporal scales which allow us evaluate how information learned by the embedding interacts with time aggregation. We report F1, Kappa, Accuracy, and macro AUC metrics following previous work (Haresamudram et al., 2022; Yuan et al., 2024; Xu et al., 2024). Ranges per metric are obtained by retraining the probe five times and calculating the mean and standard deviation.
4.2.2 BENCHMARKING EVALUATION SET-UP
Comparison to a Prior Large-Scale Pre-trained Model: Yuan et al. (2024) proposes an large- scale model with pre-trained on the UK BioBank (Bycroft al., 2018) dataset. They able to

---

Published as a conference paper at ICLR 2025
Gai Moric Regecsios (Wiis--Waai))
Stiie Velecity
Suppor:
Model
JME
.MNE
15DAE
IMSE
5DSE
1.MAE
SinCLR
0121 a 0002
.0244 0019
.927 a. 0030
.0736 0007
.454 a .0133
.0116 0001
0025
0001
.0377 0009
.0254 2. 0006
a .0.36
Aug Pizd
0114 a. 0000
J02+1 0000
.994 0000
(07-9 à. 0005
7950 a 0035
.0015 0000
.002
0001
0226 à 0002
0051
0003
.7214 2.029
RIRAA
0117 + 19910
11761
(8055
J838 J9942
(818 + 1832
7851 + 01
(0116 (600)
18326
(800
0116 + (931
0054
1806
8817 (MMA
ReiCon (ours)
.0155 4 .0005
.0190 s. 0913
JU533 2 0009
2 .016
.841 4 00299
.0014 .0000
.0034 2 0000
J023
2 0004
.029 : 0004
.7589 2.02
Table 1: Gait Metric Regression. Mean and Standard Deviations of Squared Error and Absolute Error were calculated by aggregating across each user and are related to bias and variance, respec- tively. The strong consistent performance of RelCon shows that our model able effectively capture the user-specific and temporally-preciss information needed for gait metric regression.
(9) RelCion
REBAR
(c) AugPoed
Figure 3: Correlation between predicted and true DST. RelCon has the highest correlation.
train over 100k unique participants for total of 700k unique participant-days. Their model is based upon an augmentation prediction approach with a ResNet-18 backbone, and they use it to train and embed 10s subsequences of 3-axis accelerometry signal collected at the wrist.
Following their evaluation methodology, we directly compare our RelCon foundation model against their model along two different dimensions: fine-tuning the entire model and using MLP probe. We exactly mimic their cross-validation splits on the Opportunity (Roggen et al., 2010) and PAMAP2 (Reiss, 2012) activity classification datasets and use these splits to generate the metric ranges. We also match their 10s length used for inference.
Comparison an Accel SSL Benchmarking Study: Haresamudram et (2022) seeks assess the state of the accelerometry SSL field, in the context of human activity recognition. To this end, they benchmark diverse range of self- supersuuee methods, including our aforementioned methods, SimCLR (Tang et al., 2020) and Augmentation Prediction (Saeed et al., 2019), as well as, SimSiam (Chen & He. 2021), BYOL (Grill et al., 2020), MAE (Haresamudram et al., 2020), CPC (Hare- samudram et al., 2021), and Autoencoder (Haresamudram et al., 2019). Each of their approaches uses unique backbone architecture that corresponds to the original work. They pre-train their SSL methods on the 3-axis mrint-mounted accelerometry signals from Capture-24 dataset (Chan Chang et al., 2021), 1), which is composed of 151 unique participants for a total of 44k participant-houu
Similar to RelCon method, each of their models pre-trained on wrist data. However, during evaluation, each activity classification dataset corresponds to accelerometry signals collected at dif- ferent body positions. We compare our RelCon FM with downstream dataset each benchmarked position: HHAR (Blunck et al., 2015) for Wrist, Motionsense for Waist (Malekzadeh et al., 2018), and PAMAP2 (Reiss, 2012) for Leg. In this way, each method will be evaluated on potentially dif- ferent sensor position as its pre- pre-training data. We adopt the same exact cross-vals cooss-validation splits in the original work and use them generate our ranges. We match their 2s length used for inference.
RESULTS AND DISCUSSION
5.1 TASK DIVERSITY EVALUATION RESULTS
Gait Metric Regression: Table shows the gait metric regression performance each SSL method.
RelCon had the strongest consistent performance across both gait metrics (1st in 3/5 evals for stride velocity and 1st 5/5 evals for DST), While SimCLR was not specifically designed for gait analysis (Tang et al., 2020), it achieved competitive performance for stride velocity with the highest corre- lation. This suggests that SimCLR effectively captures overall movement patterns, but may lack the nuanced understanding required for DST, which has more complex gait dynamics compared to velocity. DST measures the brief phase when both feet are on the ground, requiring high tempo- ral resolution to capture subtle transitions between stance and swing, influenced by neuromuscular

---

Published as a conference paper at ICLR 2025
AHMS Activity Classification (Wrist+WWnin
Subsequence Level
Workout Level
Model
F1
1 Kappa
f Acc
f AUC
1 F1
1 Kappa
1 Acc
AUC
SimCLR
36.35 1 .42
39.32 :
37.32 .29
.372 + 0004
50.06 4 .44
49.66 4 333
53.09 4 .30
.8855 1 00033
Aug Pred
34.48 4: 088
30.69 a 12
35.02 4.11
.8175 4 0003
54.63 4: .79
52.44 a .65
55.71 a: 600
.949 1 .0000
REBAR
36.39 2.2
32.75 = 23
36,96 1.06
.3334 2 0011
50.29 4 .78
48.53
51.94 4 .87
.8755 2 0035
RelCon (ours)
38.56 .33
3
20
29.15
.8417 .0009
55.28 86
53.87
56.94 + .68
.9778 .0166
Table 2: Field Human Activity Recognition across 16 Classes. RelCon achieves consistently strong performance when evaluated both at the subsequence-level and workout-level. SimCLR does well at the subsequence-level. but performs much worse the workout-level. The opposite is true for AugPred. This shows that there exist nuances captured by our models at both levels.
(0) RelCion
(b) REBAR
(c) AugPred
(d) SimCLR
6.0
62-
Make 1200
1.80
Patt and 1.00
Figure 4: ROC curves of Workout-Level Field Human Activity Recognition. Among other gains, RelCon is able to clearly better classify stair climbing (purple) compared to other approaches. Further class-specific prediction results can be found in the confusion matrices found in Fig. 5
coordination (Couto et al., 2023). In contrast, stride velocity is determined by cadence, making it simpler to model via frequency (Kirtley et al., 1985). REBAR, despite using the same learnable distance as RelCon, performed worse across most metrics due to differences in the loss functions (i.e. >.5 difference in corr for both velocity and DST). The hard contrastive loss used REBAR relies on an absolute set of positives/negatives, limiting its capacity to capture fine-grained relational differences. In contrast, RelCon's 's relative loss enabled better modeling of subtle differences of tran- sient sppject-specific gait dynamics, particularly necessary for modeling the complex neuromuscular functions that control DST, achieving the best performance across all 5 evaluations.
Field Human Activity Recognition: Table presents the activity classification performance across subseq and workout levels on real-world field data across 16 classes. Fig. 4 shows per-class perfor- mance and Fig. 5 shows detailed class predictions breakdown via a confusion matrix.
Table 2 shows that SimCLR showed strong performance at the subseq-level, with 2nd+ place across the eval metries. However, performance dropped to 3rd- in workout-level classification. Conversely, AugPred is 2nd place in workout-level, but dropped to the worst method at the subseq-level, across all eval metrics. These differences highlight how varying temporal resolutions capture different aspects of activity dynamics, influencing model performance across time scales.
RelCon continues to achieves state-of-the-art performance with 1st place across 7/8 eval metrics. It also has the best semantic cluster separability in its embedding space, as seen in Fig. 1. As an example, Fig. shows that RelCon is better at classifying stair climbing with a class AUC of .81 vs. .68/ / .69 / .56. Fig. 5 shows that the other SSL methods will often confuse stair climbing with climbing or elliptical, but RelCon can capture subtle differences between the slow, deliberate hand swinging motions present in those three classes. Fig. 5 also shows that RelCon more accurately distinguishes between outdoor and indoor running (avg acc normed across the two: .69 vs. .59 / .6 / 66)), likely due to its ability to detect the subtle more-uniform motion patterns characteristic strong performance across temporal resolutions and diverse activity categories underscores its robustness and generalizability in activity recognition.
of indoor running. Overall, RelCon's 's
5.2 BENCHMARKING EVALUATION RESULTS
Results from Comparison to Prior Large-Scale Pre-trained Model: Table 3 shows that RelCon achieved a stronger performance across all datasets and evaluation methods. We used a ResNet-34 based architecture with a final embedding dimension of 256 and 3.96M parameters, while Yuan
8

---

Published as a conference paper at ICLR 2025
Opportunity (Wrist-Wrist)
PAMAP2 (Wrist-Wriis
Eval Method
Pre train Data
FF
1 KKappa
1
Kappa
RelCon FM
MLP Probe
AHMS U UKBioBank
69.1 8.3
62.1 6.2
3.6
84.7
Yuan al. (2024)'s FM
MLP Probe
57.0 7.8
43.5
72
7.77
RelCon FM
Fine-tuned Fine-tuned
AHMS
98.4
97.9 0.8
98.8 1.3
98.6 1.6
(2024)'s
UKBioBank
78,9
76.9
Table 3: RelCon FM compared to Prior Large-Scale Pre-trained Model. Although the RelCon FM embeds time-series into 256 dim vectors, smaller than Yuan et al. (2024)'s 1024 dim vector, the frozen RelCon FM representation achieves stronger MLP probe performance.
HHAR (Wiist-Wrist)
Motionsonse (Wrist- Waist)
PAMAP2 (Wrist-LLgg)
F1
F1
FI
RelCon FM
$7.63 + 3.24
80,35 = 0.71
53.98 0.76
Hunsamadaa donnnadddmm oe al. (20023
Aug Pred
1.14
Sele-supervised ised w/ Fruzen Embeddiing + Linear Probe
SimCLR
55.93 1.75
83.93 1.78
50.75 + 2.97
SimSiam
45.36
71.91 12.3
47.85 2.48
BYOL
40.66
66.44
43.89 3.35
MAE
43.48 2.84
61.14 3.45
42.32 1.63
CPC
$6.24 0.98
72.89 2.06
45.84 1.39
Autoencoder
53.57 + 1.14
55.12 3.46
50,79
1.09
DeepCoavLS
$4.39
0.85
51.22
1.91
Fully Supervised
Cuny classifier
$5.43
89.25 a 0.50
59.76 1.53
1.STM classifier
37.42
86.74
Table 4: RelCon FM compared an Accel SSL Benchmarking Study, The RelCon FM has the strongest consistent performance, even outperforming the fully-supervised model when the pre- training and target domains match, with both the pre-tran pre-training dataset and downstream HHAR dataset originating from wrist sensors (Wrist-Wrris)..
al. (2024) used a ResNet-18 based architecture with an embedding dimension of 1024 and 10M parameters. Although embedding vector had lower dimensionality, RelCon FM has stronger performance with the MLP probe (69 vs 57 85 vs 73 for Opportunity PAMAP2). ReICon-learned representations able to efficiently capture the most important properties of the sequences. Then, with fine-tuning, RelCon can achieve even stronger performance due to the strong initialization.
Results from Comparison to Accel SSL Benchmarking Study: Table 4 compares the RelCon FM against a suite of SSL methods benchmarked in Haresamudram et al. (2022). Our RelCon FM shows strong generalizability, even when there sensor position mismatch between training and evaluation, as seen in PAMAP2. When the target dataset's sensor position matches the pre- training domain, seen in HHAR, RelCon acheives exceptionally strong performance, even beating the fully-supervised methods. Although SimCLR outperforms RelCon in Motionsense, there is a considerable gap between SinCL.//Relcon and the rest of the methods.
5.3 ABLATION STUDY RESULTS
The Importance of Motion Augmentations: Invariances capture conceptually important infor- mation for motion data, and augmentations have been used extensively in prior approaches to exploit this (Yurtman & Barshan, 2017; Florentino-Liaño al., 2012: Zhong & Deng, 2014). For example, learning a 3D rotation invariance makes embeddings robust to how users wear their devices. Rel- Con uses these augmentations while learning the distance function, so that the distance function can identify instances that represent similar characteristics but differ from semantic- preserving transfor- mations. Removing these augmentations from the distance function learning significantly degrades performance (> 12% drop in 3/4 tasks, shown in w/o Augmentations in Table 5), because the distance function fails to compare and identify semantically-sinn simila instances.
Variations in time-series signals stem from both within- user and between-user dynamics. Learning embeddings by comparing subsequences within the same user helps capture important temporal dynamics, as shown in the w/o Sampling Within-Subject ablation in Table 5. In this ablation, we no longer draw candidates from the within-user, across time. Instead the candidates are drawn from other batch members and augmented versions of them, similar to SimCLR. This leads to a 4% drop in subsequence- drop in workout-level classification. These results highlight the importance of modeling within-
Within-Person
Dynamics in Time-series:
classification (AHMS (EMMS-Subseq) and 7%

---

Published as a conference paper at ICLR 2025
Double Support Time
Subseq-level Activity
Workout-level Activity
Stride Velocity
Corr
1 Corr
t F1
1 FI
RelCon
0.8431
0.7559
38.56
55.28
Augmentations
-5.42%
-13.88%
-12.5%
-17.93%
we RevIN
-11.66%
-6.01%
-3.81%
-4.11%
w/o SparscMax
-0.87%
-3.7%
-5.06%
-7.11%
w/o Sampling Within-Suhject
-1.40%
-1.40%
-4.9%
-7.25%
w/ Softer Metric Loss (Kim el al., 2019)
-3.64%
-6.11%
-1.45%
1.39%
w/ Harder Binary Contrastive Loss (Xu et al., 2024)
-6.86%
-9.82%
-5.63%
-9.03%
Table 5: Ablations of Key RelCon Components. Removing augmentations in our distance learn- ing approach results in a large drop in performance, highlighting the importance of learning a motif- based distance that is robust to accel-specific invariances. RevIN strongly affects the regression tasks. Within-subject interactions and SparseMax are important for classification. Finally, our rela- tive loss strikes good balance between hardness of the pos/neg psuedo-labels in the loss function.
user dynamics over time, especially for longer-scale workout-level tasks. This is also reflected in SimCLR's performance in Table 2: SimCLR does not capture within-user interactions and also performs worse at the workout-level compare to the subsequence-lewe task
Table 5 shows that if we remove the reversible instance normaliza- tion, RevIN, component or replace the SparseMax with the traditional softmax operator within our distance function, then performance suffers. Removal of RevIN causes 12% drop in the veloeity correlation, which shows the importance of capturing relative magnitude for predicting velocity.
Refining our Learned Distance:
Relative Contrastive Loss is Good Balance of "Softness": RelCon's relative contrastive loss is a softer version of REBAR's hard binary contrastive loss. Both use a learnable distance to identify positives and negatives, but REBAR selects only one positive candidate, labeling the rest as negative, increasing the risk of false positives and negatives (Xu et al., 2024). False positives occur when a candidate incorrectly identified as positive despite belonging to a different class, while false negatives arise when multiple valid positive candidates exist but only one is selected. The softer relative contrastive loss captures the relative "positiveness" of candidates, reducing the impact of false pseudo-labels. As shown in w/ Harder Binary Contrastive Loss in Table 5, RelCon's 's relative contrastive loss improves performance >5% compared to the REBAR binary contrastive loss.
Alternatively, the relative loss function could be softened further with metric loss function that directly learns the distances in the embedding space to be proportional to distances from the learned
distance function. The distance function is now treated truth. In w/ Softer Metric Loss in Table 5, we replace the loss with the log-ratio metric loss, log( ((N...)))))) ))² from Kim et al. (2019). This re- sults performance drop across all tasks, particularly gait metric regression. This implies that a softer metric loss too exactly matches the distance function, obscuring the subtle features needed to differentiate gait. We aim for balanced approach that allows the distance function flexibly capture semantic groups, without strictly constraining the embedding space.
exact ground
((anc)= Exp.. (Iog( --
6 CONCLUSIONS
We introduce RelCon, the first foundation model for accelerometry data with state-of-the-art per- formance on multiple diverse downstream motion tasks. RelCon embeddings exhibit consistently strong performance using only simple probes for diverse tasks including stride velocity, double sup- port time, and human activity recognition, on multiple datasets. This demonstrates RelCon's ability to capture fundamental properties of motion that are relevant across tasks. We achieve this through distance learning approach that incorporates augmentations to learn sensor invari- ances, along with relative contrastive loss approach for learning fine-grained interactions between- and within-subjects, Our results show that RelCon is highly effective for motif-based time-series data, such accelerometry, and could potentially be effective for other quasi-periodic biosignals.
novel
10

---

Published as a conference paper at ICLR 2025
7
REPRODUCIBILITY
The code for our RelCon foundation model can be found at / com com/maxxu0$ re relcon. This includes the RelCon training methodology, architecture, well as the reproducible evaluation code from our "Benchmarking Evaluation" task, which utilizes public datasets. This can be used by the broader research community by providing code easily re-train the RelCon approach in addition to providing unified benchmarking task to guide model development.
8 ACKNOWLEDGMENTS
Thank you Di Wu, James Ochs, Sunny Chow, and Guillermo Sapiro Apple for their support in this work. Special thanks as well to Harish Haresamudram for his help in replicating prior results, as well as Catherine Liu for her help proofreading and editing.
Research reported here was supported by Apple and partially supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-2033655 and the National Insti- tutes of Health (NIH) under award P41EB028242. The opinions expressed in this article are the authors' own and do not reflect the views of Apple, NSF, or NIH.
REFERENCES
Salar Abbaspourazad, Oussama Elachqar, Andrew Miller, Saba Emrani, Udhyakumar Nallasamy, and Ian Shapiro. Large-scale training of foundation models for wearable biosignals. In The Twelfth International Conference on Learning Representations, 2024. URL https:// openreview
Hind Alobaidi, Nathan Clarke, Fudong Li, and AbduIrahman Alruban. Real-world smartphone- based gait recognition. Computers & Security, 113:102557, 2022.
Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Hao Wang, Michael W. Mahoney, Kari Torkkola, An- drew Gordon Wilson, Michael Bohlke-Schneider, and Yuyang Wang. Chronos: Learning the language of time series, 2024. URL https://: /ttp:s:////xvvv////// www.vvv.rrggasss 2t13://b/22233..1155
Apple. May 2021. URL
Measuring_Wall werring_Wallee See_Walking
pdf..
Henrik Blunck, Sourav Bhattacharya, Thor Prentow, Mikkel Kjrgaard, and Anind Dey. Heterogeneity Activity Recognition. UCI Machine Learning Repository, 2015. DOI:
Rishi Bommasani, Drew A Hudson. Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. On the opportu- nities and risks of foundation models. arXiv preprint arXiv:2108.072 2021.
Yonatan E Brand, Felix Kluge, Luca Palmerini, Anisoara Paraschiv-lonescu. Clemens Becker, An- drea Cereatti, Walter Maetzler, Basil Sharrack, Beatrix Vereijken, Alison J Yarnall, et Self- supervised learning of wrist-worn daily living accelerometer data improves the automated detec- tion of gait in older adults. Scientific Reports, 14(1):20854, 2024.
Clare Bycroft, Colin Freeman, Desislava Petkova, Gavin Band, Lloyd T Elliott, Kevin Sharp, Allan Motyer, Damjan Vukcevic, Olivier Delaneau, Jared O' O'Connell, et al. The uk biobank resource with deep phenotyping and genomic data. Nature, 562(7776):203-209 2018.
Rhiannon A Campbell, Elizabeth J Bradshaw, Nick Ball, Adam Hunter, and Wayne Spratford. Ef- fects of digital filtering on peak acceleration and force measurements for artistic gymnastics skills. Journal of Sports Sciences, 38(16):1855 1et.-18899.... 2020.
S Chan Chang, R Walmsley, J Gershuny, T Harms, E Thomas, K Milton, P Kelly, Foster, A Wong, N Gray, et al. Capture-24: 24: Activity tracker dataset for human activity recognition. Nature Scien- tific Data, 2021.
11

---

Published as a conference paper at ICLR 2025
Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 15750-15758, 2021.
Yuqing Chen and Yang Xue. A deep learning approach human activity recognition based on single accelerometer. In 2015 IEEE international conference on systems, man, and cybernetics, pp. 1488-1492. IEEE, 2015.
Jen-Tzung Chien and Kuan Chen. False negative masking for debiasing in contrastive learning. In 2024 International Joint Conference on Neural Networks (IJCNN), pp. 1-6. IEEE, 2024.
Ana GB Couto, Mário AP Vaz, Liliana Pinho, José Félix, Juliana Moreira, Francisco Pinho, Inês Al- buquerque Mesquita, António Mesquita Montes, Carlos Crasto, and Andreia SP Sousa. Repeata- bility and temporal consistency of lower limb biomechanical variables expressing interlimb coor- dination during the double-support phase in people with and without stroke sequelae. Sensors, 23 (5):2526, 2023.
Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou. A decoder-only foundation model for time-series forecasting. arXiv preprint arXiv:2310. 10688, 2023.
Nathaniel Diamant, Erik Reinertsen, Steven Song, Aaron D Aguirre, Collin M Stultz, and Puneet Batra. Patient contrastive learning: A performant, expressive, and practical approach to electro- cardiogram modeling. PLoS computational biology. 18(2)xe 2022.
Ehab Essa and Islam R Abdelmaksoud. Temporal-channel convolution with self-attention network for human activity recognition using wearable sensors. Knowledge-Buu Systems, 278:1 10867, 2023.
Blanca Florentino-Liafio Niamh O' M'Maooy,, and Antonio Artés-Rodríguez. Human activity recog- nition using inertial sensors with invariance sensor orientation. In 2012 3rd International Workshop on Cognitive Information Processing (CIP), pp. 1-6. IEEE, 2012.
Biying Fu, Naser Damer, Florian Kirchbuchner, and Arjan Kuijper. Sensing technology for human activity recognition: A comprehensive survey. Ieee Access, 8:83791-83820, 2020.
Sara García-de Villa, David Casillas-Pérez, Ana Jiménez-Martin, and Juan Jesús Garefa- Domínguez. Inertial sensors for human motion analysis: A comprehensive review. IEEE Trans- actions on Instrumentation and Measurement, 72:1-39, 2023.
Leon Glass and Daniel Kaplan. Time series analysis of complex dynamics in physiology and medicine. Medical progress through technology, 19:115-111 1993.
Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, and Artur Dubrawski. Moment: A family of open time-series foundation models. arXiv preprint arXiv:2402.03885, 2024.
Cean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271-21288 2020.
W3C Working Group. Motion sensors explainer, Aug 2017. URL https: http://wwww////// http///ww.....c www.ww3.org/RR/ motion-sensors/
Harish Haresamudram, David V Anderson, and Thomas Plötz. On the role of features in human activity recognition. In Proceedings of the 2019 ACM International Symposium on Wearable Computers, pp. 78-88, 2019.
Harish Haresamudram, Apoorva Beedu, Varun Agrawal, Patrick L Grady, Irfan Essa, Judy Hoffman, and Thomas Plötz. Masked reconstruction based sulf-superiiiion for human activity recognition. In Proceedings of the 2020 ACM International Symposium on Wearable Computers, pp. 45-49, 2020.
12

---

Published as a conference paper at ICLR 2025
Harish Haresamudram, Irfan Essa, and Thomas Plötz. Contrastive predictive coding for human activity recognition. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 5(2):1-26, 2021.
Harish Haresamudram, Irfan Essa, and Thomas Plötz. Assessing the state of self-supervised human activity recognition using wearables. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 6 (3), sep 2022. doi: 10.1 145/3550029. URL hwwps:tppt..... /doi. org/ / 10. / 44/55/955999
Harish Haresamudram, Chi Ian Tang, Sungho Suh, Paul Lukowicz, and Thomas Ploetz. Solving the sensor-based activity recognition problem (soar): self-supervised, multi-modal recognition of activities from wearable sensors. In Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing, pp. 759-761, 2023.
Harish Haresamudram, Chi Ian Tang, Sungho Suh, Paul Lukowicz, and Thomas Ploetz. Solving the sensor- mansor-based activity recognition problem (soar): self-supervised, multi-modal recognition of ac- tivities from wearable sensors. In Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 1004-1007, 2024.
Xinxin Huang, Yunan Xue, Shuyun Ren, and Fei Wang. Sensor-based wearable systems for moni- toring human motion and posture: A review. Sensors, 23(22))9047, 2023.
Tri Huynh, Simon Kornblith, Matthew Walter, Michael Maire, and Maryam Khademi. Boost- ing contrastive self-supervised learning with false negative cancellation. Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp. 2785-2795, 2022.
Yash Jain, Chi Ian Tang, Chulhong Min, Fahim Kawsar, and Akhil Mathur. Collossl: Collaborative self-supervised learning for human activity recognition. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 6(1 6(1):1-28, 2022.
Hyewon Jeong, Collin M Stultz, and Marzyeh Ghassemi. Deep metric learning for the hemodynam- ics inference with electrocardiogram signals. arXiv preprini 046600000 2023.
Xiyuan Jin, Jing Wang, Lei Liu, and Youfang Lin. Time-series contrastive learning against false negatives and class imbalance. arXiv preprini arXiv:2312. 1939. 2023.
Sungyeon Kim, Minkyo Seo, Ivan Laptev, Minsu Cho, and Suha Kwak. Deep metric learning beyond binary supervision. Proceedings of the IEEE/CVE Conference on Computer Vision and Pattern Recognition, pp. 2288-2297, 2019.
Taesung Kim, Jinhee Kim, Yunwon Tae, Cheonbok Park, Jang-Ho Choi, and Jaegul Choo. Re- versible instance normalization for accurate time-series forecasting against distribution shift. In International Conference on Learning Representations, 2021.
Chris Kirtley, Michael Whittle, and RJ Jefferson. Influence walking speed on gait parameters. Journal of biomedical engineering, 7(4):282 28, 1985.
Fatma Kunean, Yılmaz Kaya, Züleyha Yiner, and Mahmut Kaya. A new approach for physical hu- man activity recognition from sensor signals based on motif patterns and long-short term memory. Biomedical Signal Processing and Control, 78: 103963, 2022.
Seunghan Lee, Taeyoung Park, and Kibok Lec. Soft contrastive learning for time series. arXiv preprint arXiv:2312.16424 2023.
Ziyu Liu, Azadeh Alavi, Minyi Li, and Xiang Zhang. supf-supe contrastive learning for med- ical time series: A systematic review. Sensors, 23(9); 2023.
Aleksej Logacjov. sulf-supervised learning for accelerometer- human activity recognition: A survey. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 8(4), November 2024. doi: 10. 10.1145/3699767. URL https: : /dwi./i...////// wrg/10 000 1145/3699767.
Calum A. MacRae. Apple heart & movement study, Jul 2021.
URL https://
clinicaltrials, wtw.g///t///oc//aaaaeeeo
13

---

Published as a conference paper at ICLR 2025
Mohammad Malekzadeh, Richard G Clegg, Andrea Cavallaro, and Hamed Haddadi. Protecting sensory data against sensitive inferences. In Proceedings of the 1st Workshop on Privacy by Design in Distributed Systems, pp. 1-6, 2018.
Almontazer Mandong and Usama Munir. Smartphone based activity recognition using k-nearest neighbor algorithm. alggoohh. In Proceedings the International Conference on Engineering Technologies, Konya, Turkey, pp. 26-28, 2018.
Andre Martins and Ramon Astudillo. From softmax to sparsemax: A sparse model of attention and multi-lahel classification. In International conference on machine learning, pp. 1614-1623. PMLR, 2016.
Kaden McKeen, Laura Oliva, Sameer Masood, Augustin Toma, Barry Rubin, and Bo Wang. Beg- fm: An open electrocardiogram foundation model, 2024. URL httpt:://rxxiiv.coo org.bbs/ 2408. 05178.
Jamie McQuire, Paul Watson, Nick Wright, Hugo Hiden, and Michael Catt. Uneven and irregular surface condition prediction from human walking data using both centralized and decentralized machine learning approaches. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1449-1452 IEEE, 2021.
Max Moebus, Lars Hauptmann, Nicolas Kopp. Berken Demirel, Bjöm Braun, and Christian Holz. Nightbeat: Heart rate estimation from a wrist-worn accelerometer during sleep. arXiv preprint arXiv:2411.0073., 2024.
Abdullah Mueen, Eamonn Keogh, Qiang Zhu, Sydney Cash, and Brandon Westover. Exact dis- covery of time series motifs. In Proceedings of the 2009 SIAM international conference on data mining, pp. 473-484. SIAM, 2009.
Meinard Müller. Dynamic time warping. Information retrieval for music and motion, pp. 69-84, 2007.
Girish Narayanswamy, Xin Liu, Kumar Ayush, Yuzhe Yang, Xuhai Xu, shun liao, Jake Garrison, Shyam A. Tailor, Jacob Sunshine, Yun Liu, Tim Althoff, Shrikanth Narayanan, Pushmeet Kohli, Jiening Zhan, Mark Malhotra, Shwetak Patel, Samy Abdel-Ghaffar, and Daniel McDuff. Scaling wearable foundation models. In The Thirteenth International Conference Learning Represen- tations, 2025. URL https: : http///peeeeeeeee/oe/ee net forum http//ooormmmddd///o 21d-yy4006b2222222
R OpenAl et al. Gpt-4 technical report. ArXiv, 2303:08774, 2023.
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Mare Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint axXiv:2304.07133 2023.
Anton Pfeifer and Volker Lohweg. Identifying characteristic gait patterns in real-world scenarios. In PROCEEDINGS 28. WORKSHOP COMPUTATIONAL INTELLIGENCE, pp. 279, 2018.
Arvind Pillai. Dimitris Spathis, Fahim Kawsar, and Mohammad Malekzadeh. Papagei: Open foun- dation models for optical physiological signals. arXiv preprint arXiv:2410.20542 2024.
Jathurshan Pradecpkumar, Xihao Piao, Zheng Chen, and Jimeng Sun. Single-channel eeg tokeniza- tion through time- time-frequency modeling. arXiv preprint arXiv:2502.16060. 2025.
James M Rehg, Susan A Murphy, and Santosh Kumar. Mobile Health: Sensors, Analytic Methods, and Applications. Springer. 2017. doi: 00.10077978---319-1
Attila Reiss. PAMAP2 Physical Activity Monitoring. UCI Machine Learning Repository, 2012.
DOI: h00phh/:/bbbu.ogg/22222222//2
Jorge Reyes-Ortiz Davide Anguita, Luea Oneto, and Xavier Parra. Smartphone-Base Based Recognition of Human Activities and Postural Transitions. UCI Machine Learning Repository, 2015. DOI:
http::/dda.org//004444///444
14

---

Published as a conference paper at ICLR 2025
Daniel Roggen, Alberto Calatroni, Ricardo Nguyen-Dinh, Long-Vanand Vanand Chavarriaga, and Hesam Sagha. OPPORTUNITY Activity Recognition. UCI Machine Learning Repository, 2010. DOI: http:///bb.ogg/0.2433///CMM
Aaqib Saced, Tanir Ozcelebi, and Johan Lukkien. Multi-task self-supervised learning for human activity detection. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 3(2):1-30, 2019.
Mithun Saha, Maxwell A Xu, Wanting Mao, Sameer Neupane, James M Rehg, and Santosh Kumar. Pulse-ppg: An open-source field-trained ppg foundation model for wearable applications across lab and field settings. arXiv preprint arXiv:2502.01 108, 2025.
Patrick Schäfer and Ulf Leser. Motiflets: Simple and accurate detection of motifs in time series. Proceedings of the VLDB Endowment, 16(4):725-737, 2022.
lan Shapiro, Jeff Stein, Calum MacRae, and Michael O'Reilly. Pulse oximetry values from 33,080 participants in the apple heart & movement study. NPJ Digital Medicine, 6(1):334, 2023.
Aawesh Shrestha and Myounggyu Won. Deepwalking: Enabling smartphone- walking speed estimation using deep learning. In 2018 IEEE global communications conference (GLOBECOM), pp. 1-6. IEEE, 2018.
Jan Slemenšek, Iztok Fister, Jelka Geršak, Božidar Bratina, Vesna Marija van Midden, Zvezdan Pirtošek, and Riko Šafarič. Human gait activity recognition machine learning methods. Sensors, 23(2):745, 2023.
Abolfazl Soltani, Nazanin Abolhassani, Pedro Marques- Marques-Vidal, Kamiar Aminian, Peter Vollenwei- der, and Anisoara Paraschiv-lonesee Real-world gait speed estimation, frailty and handgrip strength: cohort-based study. Scientific reports, 11(1):18966, 2021.
Junho Song, Long-Hwan Jang, Byeong Tak Lee, DongGyun Hong, Joon myoung Kwon, and Yong- Yeon Jo. Foundation models for electrocardiograms, 2024. URL https: http///axxvvvv///ee abs/ 240s/2407.c0 07110.
Marcin Straczkiewicz, Peter James, and lukka-Pekka Onnela. A systematic review of smartphone- based human activity recognition methods for health research. NPJ Digital Medicine, 4(1):148, 2021.
Jan Stutz, Philipp A Eichenberger, Nina Stumpf, Samuel EJ Knobel, Nicholas Herbert, Isabel Hirzel, Sacha Huber. Chiara Oetiker, Emily Urry, Olivier Lambercy, et al. Energy expenditure estimation during activities daily living in middle-aged and older adults using an accelerometer integrated into hearing aid. Frontiers Digital Health, 6: 6:1400535, 2024.
Angela Sucerquia, José David López, and Jesús Francisco Vargas-Bonille Real- heal-liferrell-tin el- derly fall detection with a triaxial accelerometer. Sensors, 18(4):1001, 2018.
Sungho Suh, Vitor Fortes Rey, and Paul Lukowicz. Tasked: transformer-based adversarial learning for human activity recognition using wearable sensors via self-knowledge distillation. Knowledge- -aaeed Systems, 260: 10143, 2023.
Mohammad Ali Takallou, Farahnaz Fallahtafti, Mahdi Hassan, Ali Al-Ramini, Basheer Qolomany, Iraklis Pipinos, Sara Myers, and Fadi Alsaleem. Diagnosis of disease affecting gait with body acceleration-based model using reflected marker data for training and a wearable accelerometer for implementation. Scientific reports, 14(1):1075,
2024. 2024.
Chi lan Tang, Ignacio Perez-Pozuelo, Dimitris Spathis, and Cecilia Mascolo. Exploring contrastive learning in human activity recognition for healtheare, arXiv preprint arXiv:2011.11542, 2020.
Sana Tonekaboni, Danny Eytan, and Anna Goldenberg. Unsupervised representation learning for time series with temporal neighborhood coding. International Conference of Learning Represen- fations, 2021.
15

---

Published as a conference paper at ICLR 2025
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention all you need. Advances in neural informa- tion processing systems, 30, 2017.
Ning Wang, Eliathamby Ambikairajah, Nigel H Lovell, and Branko Celler. Accelerometry based classification of walking patterns using time-frequency analysis. In 2007 29th annual interna- tional conference of the ieee engineering medicine and biology society, pp. 4899-4900 IEEE, 2007.
Maxwell Xu, Alexander Moreno, Hui Wei, Benjamin Marlin, and James Matthew Rehg. Rebar: Retrieval-based reconstruction for time-series contrastive learning. In The Twelfth International Conference on Learning Representations, 2024.
Zheng Xu, Nicole Zahradka, Seyvonne Ip, Amir Koneshloo, Ryan T Roemmich, Sameep Sehgal, Kristin B Highland, and Peter C Searson. Evaluation of physical health status beyond daily step count using a wearable activity sensor. npj Digital Medicine, 1(1):164, 2022.
Shu-wen Yang, Heng-Jui Chang, Zili Huang, Andy T Liu, Cheng-I Lai, Haibin Wu, Jiatong Shi, Xuankai Chang, Hsiang- Tsai, Wen-Chin Huang, et al. A large-scale evaluation of speech foundation models. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2024.
Hang Yuan, Shing Chan, Andrew P Creagh, Catherine Tong, Aidan Acquah, David A Clifton, and Aiden Doherty. Sele-super learning for human activity recognition using 700,000 person- days of wearable data. NPJ digital medicine, 7(1):91, 2024.
Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, and Bixiong Xu. Ts2vec: Towards universal representation of time series. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pp. 8980-8987, 2022.
Aras Yurtman and Billur Barshan. Activity recognition invariant to sensor orientation with wearable motion sensors. Sensors, 17(8):1838, 2017.
Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis, and Marinka Zitnik. Self-supervised con- trastive pre-training for time series via time-freyuency consistency. Advances in Neural Informa- fion Processing Systems, 35:3988-4003, 2022.
Yu Zhong and Yunbin Deng. Sensor orientation invariant mobile gait biometrics. In IEEE interna- tional joint conference on biometrics, pp. 1-8. IEEE, 2014.
16

---

Published as a conference paper at ICLR 2025
A APPENDIX
A.1 EXTRA AHMS FIELD HUMAN ACTIVITY RECOGNITION RESULTS
(a) RelCon
(b) REBAR
GCP coe 500
GCL
600
C:00 S:C
600
Buacled 1.21 C:32
MI
COL
3MM
Aoorig AZI GCL 000
552
000
640 oM
Serwen AMI GCL 042 940 6XL
040
60 COR
6000 000
180g
HR AZI KET 010
SAL
(c) AugPred
(d) SimCLR
JMCC BED G01
550
0/0
OUE J.M 120
indertocigg
0.0
016
130
Maacarsag
MCL OIN XM KLH
01
O0L
033
6.0
000
000
0.0
#aackssiing
300
030
1.00
0.0
038
130
ME SKE
550
030
arthomskong
E40
AM
mecomueing
321 641 031
03
330
zwrnning
0:00
600
SXL
030
031 1.0
recoespe
Figure 5: Confusion Matrices for AHMS Field Human Activity Recognition at the Workout Level. We can see RelCon has the best performance. Unlike REBAR and AugPred, RelCon can better predict Outdoor running from Indoor Running. RelCon is also able to better predict Stair Climbing unlike the others.
A.2 MODEL. IMPLEMENTATION DETAILS
SimCLR: We follow the approach described by Haresamudram et al. (2023), following the implementation located here: https: //t/t//ttbbbb// com/ /bbi/biiiiio compsoattutoriall al/soar__ attpiiaaatteeemainnn simclr. Then we use a batch-size of 64, temperature of 1, and train for le5 steps.
Augmentation Prediction: We follow the approach described by Yuan et al. (2024), following the implementation located here: https:/ / /t//ttttubbbb/// com /wt/e//aweeeeee//eaaaaaaaaa selee/sssswwaaaeseee sssi-weaaablls?? tab=readme-ov- file. Then we use a batch-size of 64 and train for le5 steps.
REBAR: We follow the approach described by Xu et al. (2024), following the implementation located here: https: / //gtttb.... www.oom//ao........ http/m/xx000/// maaxx05/reea.... rebar. Then we use the prior SimCLR aug- mentations, a batch-size of 64, temperature of 1, candidate set size of 20, and train for le5 steps.
17

---

Published as a conference paper at ICLR 2025
RelCon: We the augmentations utilized by the aforementioned SimCLR model, batch-size of 64, temperature of I, candidate set size of 20, and train for 1e5 steps. Please refer to our code at https://githut.
http://twwh....ooooooeeeeeeeemee www//aowwo//////cccc/// wew.suuuuo//eeooooooo for further details.
A.3 DETAILS AND PREPROCESSING ON THE AHMS PRE-TRAINING DATASET
Please refer to original paper on AHMS for further details on the dataset (Shapiro et al., 2023). Specifically, Figure 8 in that work includes visualizations that show general subject demographic distributions including age, body mass index, and self-reported race and ethmicity.
Preprocessing: We use the raw 100hz accelerometry data as is, without specific preprocessing techniques, such as filtering or downsampling. We choose to not filter the data as prior work has discouraged filtering in a daily monitoring setting (Campbell et al., 2020), and may prevent our deep learning model from modeling subtle nuances within the signal. Additionally, we would like to develop our methods to be robust to noise via augmentations, such additive gaussian noise augmentations. We purposely do not attempt to filter out periods of low accelerometry activity. This because small, minor changes the accelerometer signal have been shown still informative, being able to predict heart rate (Moebus et al., 2024).
A.4 JUSTIFICATION FOR USING -EEELER ACCELEROMETER ONLY DATA
Motion information can be analyzed with multiple sensor streams, whether it be through multiple accelerometer sensors strapped in different locations (Jain et al., 2022) via extra IMU-based sensors, such as the gyroscopic sensor (García-de Villa et al., 2023). Many prior state-of-the-art human activity recognition, supervised machine learning models will exploit the full sensor suite that includes multi-modality and multi-location sensors (Essa & Abdelmaksoud, 2023; Suh et al., 2023). We recognize the value of foundation model that can incorporate multi-modality and/or multi- location stream of data, as it would enable for greater insights on human motion and physiology, and we are interested in investigating this in future work.
However, for our foundation model, we strive for broad generalizability in order to ensure that our learning approach and model is applicable across various settings. This includes low resource settings that only have accelerometer sensors available, as gyroscopic sensors are quite power hungry (Group, 2017). Accelerometer sensors are thus the most common sensor for monitoring human motion (Huang et al., 2023). Additionally, world field settings, in which multi-location sensors are uncommon for daily usage due to their bulkiness and discomfort. As such, our foundation model is able to be benchmarked against broad range of datasets (i.e. our AHMS classification, our Gait Metric Data, HHAR, Motionsense, PAMAP2, Opportunity), which each utilize different sensor hardwares, but all include at least one 3-axis accelerometer sensor.
would like our model to also be applicable for real-
A.5 ELABORATING ON THE TABLE FIG.
False Negatives in a SimeLR-based approach is well-studied problem, with many recent works proposing novel methodologies to address this (Huynh et al., 2022; Jin et al., 2023; Chien & Chen, 2024). Additionally, the accelerometry SimCLR we are benchmarking (Tang al., 2020) makes no distinction to model within-user interactions, by treating every subsequence as independent, and hence does not model within-user interactions. Both SimCLR and AugPred creates instances from the original sequence via augmentations, and so they will be resistant to False Positives.
Similar to REBAR, RelCon explicitly models within and between-user interactions by explicitly comparing anchor against candidates from within-user across time and across other users. How- ever, unlike REBAR, we model the relative positions of our candidates, rather than a binary com- parison that treats all negative instances as the same. Then, RelCon is more likely to be resistant to False Positives and False Negatives due to the enhanced comparison that captures the nuanced differences between candidates.
18

---

Published as a conference paper at ICLR 2025
A.6 COMPUTATIONAL COMPLEXITY OF RELCON
During training, RelCon needs to compare the relative distances of each candidate from the an- chor with our distance function. The distance function utilizes a highly parallelizable transformer function with complexity of O x d)(Vaswani et al., 2017) and a convolution to embed the in- puts with a complexity of Ol T d') (Vaswani et al., 2017). T=256 the subsequence length, d=64 the embedding dimension, and k=15 the kernel size. Therefore, because we calculate this distance function for every candidate, given a size of c, our total complexity during training is ( x T x + x h x T x d²². In our future research, we will work on decreasing the computa- tional cost during training.
A.7 EVALUATION DATASET DESCRIPTIONS
A.7.1 COMPARISON TO PRIOR LARGE-SCALE PRE-TRAINED ACCEL MODEL (YUAN ET AL., 2024)
Yuan et al. (2024) has released their code publicly here, which contains exact data split and gener- ations: https:/ / g pi thub. cow//xWearabbl/cccccccmoom/amooa
ssssswearrbbllee Our codebase uses their exact splits'. Table 3 in our paper is then constructed by drawing from Table 2 in their original paper. Note that in Yuan et al. (2024), MLP Probe referred to as "Fine-tuned self-supervised after follows:
ConV layers". The used dataset details
Opportunity (Roggen et al., 2010): folold ceave-one-subject-ee out cross validation with the sitting, standing, walking, and lying labels.
PAMAP2 (Reiss, 2012): 9-fold leave-one-subject-our cross validation with the lying, sitting, standing, walking, ascending stairs, descending stairs, vacuum cleaning, and ironing classes. This is the wrist-specific accelerometry data.
A.7.2
COMPARISON TO AN ACCEL SSL BENCHMARKING STUDY (HARESAMUDRAM ET AL.,, 2022)
Although Haresamudram et al. (2022) has not released their code publicly, contacted the authors directly to ensure that we matched their exact splits and classes evaluated. Our codebase uses their exact splits Table 4 in our paper is then constructed by drawing from Table 3 and 4 in their original paper. Specifically, HHAR is drawn from Table 4, PAMAP2 from Table 3/4, and MotionSense from Table 3 (Haresamudram al., 2022). Note that in Haresamudram et al. (2022), Augmentation Prediction is referred to as "Multi-Tase Self Supervision". The used dataset details are as follows:
HHAR (Blunck al., 2015): 5-fold leave-subject-out cross validation with the bike, sit, stairs down, stairs up. stand, and walk classes.
Motionsense (Malekzadeh et al., 2018): 5-fold sune--subect-out cross validation with the down- stairs, upstairs, sitting, standing, walking, and jogging labels.
PAMAP2 (Reiss, 2012): 5-fold save-subject-out cross validation with the lying, sitting, standing, walking, running, cycling, nordic walking, ascending stairs, descending stairs, vacuum cleaning, ironing, and rope jumping classes. This is the ankle-specific accelerometry data.
A.8 GENERALIZING TO TIME LENGTHS BEYOND 2.56 SECONDS
2.56s is common accelerometry subsequence size for motion tasks (Reyes-Ortiz et al., 2015; Chen & Xue, 2015; Wang et al., 2007; McQuire et al., 2021; Mandong & Munir, 2018). Additionally, we have shown that our approach can easily be used for time-series with other, differing lengths. In our comparisons against Yuan et al. (2024), we utilize a subsequence length of 10 seconds in order to match their evaluations, and we still show strong performance. In our comparisons against Haresamudram et al. (2022), we utilize subsequence length of 2 seconds in order to match their evaluations. This highlights that our model is robust to varying input lengths and is generalizable across input data configurations, -lveallvveragee--ee avvrraee-oooling layer that we have at the end of our architec-
would be desirable from a foundation model. This flexibility is
enabled by the final ture. Additionally, in our AHMS workout-levee classification task, we show how our method can be used with variable-length time-series that can last up to 10 minutes long by aggregating predictions across windows.
19


Reviews:
[{"review_id": "gGtePOWqPj", "reviewer": "Reviewer_21L2", "summary": "This paper presents RelCon, a contrastive learning approach that leverages relative similarity between different accel signal segments (motifs) as a softened discriminative loss in contrastive learning. Authors use the proposed contrastive learning setup to pre-train a 1-D ResNet-34 backbone on 2.56 s long 100 Hz accel signal segments. The resulting pre-trained model generated a 256-dimensional embedding representing the accel signal segment. This pre-trained model was kept frozen and a linear layer was trained for downstream tasks, such as activity classification and gait metrics regression. \n\nAuthors show the effectiveness of their method via rigorous comparisons with SSL works (REBAR, AugPred, SimCLR) as well as supervised training approaches on the downstream classification tasks.", "questions": "* Why is REBAR not included as a baseline in Table 4?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "* The paper is very well-written. The authors clearly explain their approach, what parts are similar to the past work, and where they have improved over the past work. \n\n* This paper has strong empirical results. I think, the approach is most similar to REBAR (which used motif-based similarity in contrastive loss) and AugPred (which pre-trained another accel-based foundation model). However, authors show that RelCon achieves much better empirical results compared to both REBAR and AugPred.", "weaknesses": "I think, the only weakness of the proposed method is that it can be seen as a somewhat straightforward extension of REBAR by i) using the relative distance as a soft metric instead of hard similarity labels, and ii) a few additional enhancements that were also proposed independently by others earlier (sec 3.1). However, authors clearly quantify the benefits of RelCon over earlier works via rigorous empirical evaluations where it outperforms AugPred, SimCLR, and REBAR in most cases.", "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": "", "extra_scores": {}}, {"review_id": "zSQ0sNeb90", "reviewer": "Reviewer_afi1", "summary": "The current paper presents a foundation model for acceleration data on wrist wearables applied to human activity recognition. In this case, focus on the classification of a broad set of human activities (e.g, walking, stair-climbing, swimming,..).\nThe main contribution is an approach to include augmented data for enhancing the learning of similar samples systematically distorted. And a new formulation for a relative contrastive loss that encodes relative order relationships into the standard cross entropy loss.\nThe results of the foundational model proposed trained on 1 Billion segments of data of wrist acceleration (equivalent to 82 years of data from 87k participants).", "questions": "1. What is your definition of a Motif,?\n2. How could it be proven in this work that distance measures actually reflect similarities in motifs?\n3. Why is the title \"motion Foundation Model for wearable data\" → your current work although extensive data was trained comprises \"Wrist acceleration data\" not any other modality even from IMU, thus it reads incorrectly.\t(2,56 s segments of 100 Hz x,y,z accelerations) \n4. In Figure 1 is there strict proof of all the characteristics listed here? I would like to see each of them analyzed more clearly in the conclusions.\n5. What is the size of the dataset and classes used for evaluation on HHAR, and PAMAP2?\n6. What is the distribution of data on each of these datasets? Balance on classes?\nIs PAMAP2 wrist or ankle data used? There is a discrepancy between Table 3 and the description in the text.\n7. What is the inference time, and computational complexity of the proposed approach RelCon? Is there any advantage over REBAR or SimCLR?\n8.1 On Fig. 2, why is it concluded that the representation is clearer? I fail to see a difference compared with REBAR.\n8.2. On the t-SNE, what perplexity was used? Are the results consistent at all levels?", "limitations": "", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. A foundational model trained on a large data sample over 82 years of data from 87k participants\n2. An interesting approach for training through self-supervised learning data augmentation.\n3. Introducing a loss function (relative contrastive loss) that accounts for augmented points distance to the anchor for improving training, thus making it resistant to false positives.\n4. Comparative analysis with similar approaches\n5. Performance proved on the classification of motion tasks as well as regression of 2 metrics in walking.", "weaknesses": "1. The learning approach is only tested on only 2 datasets of a single sensor, thus the idea of an acceleration foundational model is not proven as a generalization is still questionable. Others such as: PAMAP2, USC-HAD, MHealth. \n2. One suggestion would be to evaluate other acceleration-based HAR with multiple signals in benchmarks of HAR that go beyond single sensor-based. \n3. Improvements to previous work seem marginal +0.01 AUC and 2.17 F1-score improvement for classification tasks on the AHMS dataset. \n*3. It would be ideal to conduct statistical significance tests on these improvements and discuss the practical implications of these improvements. Also, please highlight the meaning of your improvement in some specific classes (does it improve greatly in one class?). Rather than relying only on F1 scores.\n4. Results comparing to wrist acceleration models across the literature were limited to SimCLR and REBAR. And the analysis compared to supervised learning was minimally addressed. \n*4 * I would recommend comparing the performance in some of the SOTA best-trained models for activity classification: e.g, F1-scores on PAMAP2 0.86 (Essa & Abdelmaksoud, 2023), MHealth: 0.94(Suh et al., 2023), 0.83 USC-HAD (Essa & Abdelmaksoud, 2023), and so.\n\nReferences: \n[1] Ehab Essa and Islam R. Abdelmaksoud. Temporal-channel convolution with self-attention network for human activity recognition using wearable sensors. Knowledge-Based Systems, 278:110867, 2023. ISSN 0950-7051. doi: https://doi.org/10.1016/j.knosys.2023.110867. \n[2] Sungho Suh, Vitor Fortes Rey, and Paul Lukowicz. Tasked: Transformer-based adversarial learning for human activity recognition using wearable sensors via self-knowledge distillation. Knowledge- Based Systems, 260:110143, 2023. ISSN 0950-7051. doi: https://doi.org/10.1016/j.knosys. 2022.110143.", "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": "", "extra_scores": {}}, {"review_id": "UCHabNATIH", "reviewer": "Reviewer_byiS", "summary": "This paper presents a relative contrastive learning framework with motion time series data from wearables. Contrastive learning is a common technique for developing pre-trained models for motion time series data. A major component of contrastive learning for motion time series is to construct pair and negative pairs of samples to obtain some representation, which isn’t trivial for motion time series. \n\nThe main challenge is the difficulty of generating augmented views of the same time series segment without introducing artifacts. This paper builds upon an existing contrastive framework that makes use of the fact that many short segments of human activities are repeated in nature which can be used as positive pairs e.g. repeated walking, hand-shaking behaviors. The paper improved on a learnable distance metric used to select the positive pairs and introduced a soft contrastive loss so that not all the negative pairs are considered the same.\n\nThe experiments presented showed competitive performance against other pre-trained models and other self-supervised methods.", "questions": "1. In text citations have not be been correctly phrased throughout the manuscript\n2. The selection of 2.56 second as the window length requires justification. If the proposed work were to be a foundation model, it should be designed to cater for common window lengths used 1s, 5s, 10s etc.\n3. Can you share your code at least during the review process as again there are questions around your evaluation procedure which is likely due to overfitting?\n4. How are your workout labels obtained, self-reported or via some objective alternative modality?\n5. What are the population characteristics of the pre-training population?\n6. How did you process your IMU data, filtering, downsampling, non-wear detection? \n7. In table 3, can you include a statement to indicate the results from Yuan et al are quoted as you didn't implement their evaluations to avoid confusion.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper nicely incorporated characterises of motion time series into the design of the contrastive learning framework which is commendable. \n2. The empirical performance is promising.\n3. A wide range of benchmarks are included", "weaknesses": "1. Neither the code nor the model is open source, preventing future reuse. I believe at least pseudo code should be provided somewhere. \n2. The Apple Heart and Movement Study used to retrain the model was collected using consumer-grade devices in a free-living environment. Often, data coming from consumer-grade devices have limited data quality, including noise and missing data. More descriptives on the study participants, data processing pipeline, and an exploratory analysis of the IMU data will help to understand what data goes into pre-training.\n3. I believe the results shown in Table 3 when comparing the proposed model and another pre-trained model is biased for several reasons. The benchmark datasets used Opportunity and PAMAP2 are small, it is very easy to overfit ofthese two datasets. The authors are quoting performance in the high 90 of their proposed model which is a sign of overfitting already. Can the authors at least share their code during the review process so that we know how the evaluation was implemented?\n4. Another reason the benchmark is overfitted (Table 3) is that the metric difference between a pre-trained model and a non-pre-trained model is a mere 5%. Whereas the performance difference for Yuan et al is about 10%+. Furthermore, Yuan et al, 2024 was pre-trained on the UKB which is 10x the size of the Apple Heart & Movement Study, it is hard to believe the reported performance from this work is about 100% better in some cases.", "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": "", "extra_scores": {}}, {"review_id": "A8j0GQ1WIB", "reviewer": "Reviewer_DTZW", "summary": "This paper introduces a self-supervised learning approach, RelCon, that uses a relative contrastive learning method to train a motion foundation model for wearable sensor data. The model is pretrained on a large-scale dataset AHMS comprising 1B accelerometer segments from over 87,000 participants. RelCon integrates a learnable distance measure and a softened contrastive loss, which enables the proposed model to capture motif similarity and domain-specific semantic information. The evaluation demonstrates good performance of the proposed method across multiple downstream tasks, including gait metric regression and human activity recognition, compared to existing state-of-the-art approaches.", "questions": "- Please provide a fair comparison evaluation with Yuan et al. (2024) using the same network architecture.\n- Is it possible to evaluate the proposed SSL method with other SSL approaches pretraining on the same dataset like Capture-24?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ This paper presents an extensive dataset for pretraining the model. The dataset scale is substantial, and training on over a billion segments could significantly contribute to wearable motion data analysis.\n+ The proposed SSL approach with a learnable distance measure and softened contrastive loss is interesting and looks novel.", "weaknesses": "- The authors compared their ReCon foundation model trained on their AHMS dataset using ResNet-34 with Yuan et al. (2024) trained on UkBioBank Dataset using ResNet-18. Even though they argued that ResNet-34 is \"smaller and deeper\" and performs better than ResNet-18, this is not a fair comparison to show the superiority of their AHMS dataset and method. A fair evaluation should employ the same architecture (e.g., ResNet-18 for both methods) to isolate the effect of the SSL method, rather than introducing variability due to architecture differences.\n- The authors do not conduct an adequate benchmarking study on an established dataset for SSL in accelerometer data. For instance, they could evaluate their method by pretraining on the AHMS and Capture-24 datasets, allowing for a more consistent comparison to other SSL methods.", "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": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)