Motion time series collected from mobile and wearable devices such as smartphones and smartwatches offer significant insights into human behavioral patterns, with wide applications in healthcare, automation, IoT, and AR/XR due to their low-power, always-on nature. However, given security and privacy concerns, building large-scale motion time series datasets remains difficult, preventing the development of pre-trained models for human activity analysis. Typically, existing models are trained and tested on the same dataset, leading to poor generalizability across variations in device location, device mounting orientation and human activity type. In this paper, we introduce UniMTS, the first unified pre-training procedure for motion time series that generalizes across diverse device latent factors and activities. Specifically, we employ a contrastive learning framework that aligns motion time series with text descriptions enriched by large language models. This helps the model learn the semantics of time series to generalize across activities. Given the absence of large-scale motion time series data, we derive and synthesize time series from existing motion skeleton data with all-joint coverage. Spatio-temporal graph networks are utilized to capture the relationships across joints for generalization across different device locations. We further design rotation-invariant augmentation to make the model agnostic to changes in device mounting orientations. Our model shows exceptional generalizability across 18 motion time series classification benchmark datasets, outperforming the best baselines by 340% in the zero-shot setting, 16.3% in the few-shot setting, and 9.2% in the full-shot setting.
Paper
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Summary
Overall, this is a paper about human activity recognition from motion time series data. Authors design a unified framework for data generation, pre-training, and evaluation. The data used for pre-training is generated by motion skeleton data. LLM is also involved in text augmentation. Experimental results on 18 datasets demonstrate the superior performance of this paper.
Strengths
[1] This paper is well-written, figures are well-designed. [2] The idea of synthesizing data for pretraining motion time series is interesting. [3] 18 real-world datasets are involved for evaluation, multiple activities are included.
Weaknesses
Overall, I think this is an interesting paper and is promising. Therefore, I provide lots of comments. However, it has some obvious potential flaws in the current version. Please see limitations.
Questions
I have some questions regarding the experimental design, baselines, related works, novelty, etc. I put them in the limitation section.
Rating
6
Confidence
4
Soundness
3
Presentation
3
Contribution
3
Limitations
[1] The data generation is interesting. I am thinking about what if we replace the generated data with a few real data for model training, how will the performance look like. Or, please see my question in [8], how to justify the importance of synthesized data. [2] Missing related work. There are important studies of pretraining motion time series missing. Authors can check more carefully, e.g., from sensys, kdd. [3] For the random rotation method, there are multiple existing approaches. Authors need to justify the difference or add citations. For example, "Device Orientation Independent Human Activity Recognition Model for Patient Monitoring Based on Triaxial Acceleration”, “Data augmentation of wearable sensor data for Parkinson’s disease monitoring using convolutional neural networks”, and a few other papers from Mobicom and Ubicomp also applied similar methods. [4] Also, for the rotation method, does Acc and Gyro share the same rotation? [5] I am wondering is there any data normalization? Given the distribution discrepency among synthesized data and real data, it might hurt the performance. Currently, I only see a normalized matrix in Line 156. [6] For the data generation module, I like the idea of utilizing physical laws. However, it seems this generation method is not proposed by this manuscript. Therefore, authors might want to justify the technical contribution. [7] Fig 7 looks weird. Does it mean ImageBind cannot get any samples correct for most activities? Also, how the proposed method and ImageBind infer the activities not seen before should be described in more detail. [8] The performance gain of the zero-shot setting is impressive. For zero-shot setting, how are baseline models are trained? Are they fine-tuned also with the generated data? [9] In line 131, it would be great if there is some documents can support the claim. [10] I guess the authors aim to address the generalization issue of HAR, e.g., device locations, orientations. There are quite a few studies in these area, for domain adaptation domain generalization. Some of them explore augmentation, some explore pretraining, etc., to address the challenges mentioned in the introduction. [11] For zero-shot setting, the performance seems too low for real-world usage, e.g., for datasets with only 4 or 6 classes, the acc is smaller than 50%. [12] Authors mention cross-dataset a few times in the paper, is there some experiments focusing on this setting except the zero-shot experiment? [13] Another concern I have is the full-shot setting, there are a bunch of SoA models from AAAI, KDD, IMWUT. I am not sure whether the provided baselines are representative. However, it is good to see authors replace some baselines from zero-shot settings.
Summary
This paper proposes a unified pre-training framework using simulated data from body skeleton model for motion time series. The authors rightly pointed out the challenges of collecting motion time series at scale due to privacy concerns despite the fact that the motion time series data have great promises in a wide range of applications. Thus, this is an area with few pre-trained models unlike other data modalities. The simulation framework proposed was used to generate the synthetic data needed for pre-training. The author then utilised the generated motion time series in contrastive learning using a graph neural network that learns a joint embedding for sensor data at different body locations. The extensive evaluations of the pre-trained model demonstrated superior performance for human activity recognition in several settings, including zero-hot, few-shot and full-shot performance.
Strengths
* The manuscript was well-written. I thoroughly enjoyed reading it. I particularly like that the paper clearly points out the methodological gap for motion time series data to the ML audience at NeurIPS. * The simulation framework is an important contribution to the ubiquitous computing community, for which large-scale datasets are difficult to obtain. * The use of GNNs to learn a joint embedding for wearable sensors is novel. It is great that the GNN implementation can also account for downstream tasks by using a masking trick when not all sensor modes are available! * The performance evaluations were extensive in terms of both benchmark datasets and baseline models included.
Weaknesses
1. Despite my general excitement about this paper, this paper has not recognised the body of work already done in large-scale pre-trained models for motion time series. The authors might not be familiar with this area of work because existing pre-trained models generally use Biobank and are published in journals. It would be great if the authors could compare the following work in the context of their results: 1. Yuan, Hang, et al. "Self-supervised learning for human activity recognition using 700,000 person-days of wearable data." NPJ digital medicine 7.1 (2024): 91. 2. Spathis, Dimitris, et al. "Self-supervised transfer learning of physiological representations from free-living wearable data." Proceedings of the Conference on Health, Inference, and Learning. 2021. 2. Pre-training from synthetic data is a cost-effective approach. Its performance is likely to plateau at some point as compared to pre-trained using real data. It would be interesting to see a comparison with Yuan et al. 2024 as this model was pre-trained on 700,000 person-days of real data. I also appreciate that adding an experiment might not be realistic, but I do believe that it should be fairly easy to do the fine-tuning. 3. The HumanML3D human selection data only spans over a mere 29 hours. I am very suspicious that any pre-training on just 29 hours of synthetic data will be enough for pre-training as scaling laws go. Even though the current results show good performance, if I am not wrong, the majority of the benchmarks were collected in the lab, which doesn't represent realistic human behaviour in a free-living environment. CAPTURE-24 could be another interesting benchmark as it is one of the largest open-access free-living activity recognition datasets. More importantly, it would be good to get a sense of how the downstream performance scales to your pre-training data so that we can determine whether the benefits of pre-training have plateaued. 4. Also, you probably don't have to add the references below, but I just want you to know that there are several biobanks that contain large-scale wearable sensor data that can be used for pre-training. Your work is still valuable as your can support multi-sensors, whilst most of the existing large-scale datasets only contain wrist-worn devices. 1. Doherty, Aiden, et al. "Large scale population assessment of physical activity using wrist worn accelerometers: the UK biobank study." PloS one 12.2 (2017): e0169649. 2. Chen, Yuanyuan, et al. "Device-measured movement behaviours in over 20,000 China Kadoorie Biobank participants." International Journal of Behavioral Nutrition and Physical Activity 20.1 (2023): 138. 3. Master, Hiral, et al. "Association of step counts over time with the risk of chronic disease in the All of Us Research Program." Nature medicine 28.11 (2022): 2301-2308.
Questions
1. What your input data format during fine-tuning and sampling frequency? 2. Are Model weights and pre-trained model made available?
Rating
7
Confidence
4
Soundness
3
Presentation
4
Contribution
4
Limitations
The authors have addressed this adequately.
Thank you for all the additional experiments that provided pre-training based on synthetic data compared to biobank pre-training. I just have two more comments and questions: 1. In your Figure 1 comparing BioBankSSL and UniMTS, the few-shot few-tuning performance might be too good to be true, at least in the first instance. How are you doing the train/test split? Currently, the axis is the number of samples from each activity class. If your train/test split is not subject-wise, then the test result is biased. 2. When you incorporated real data for pre-training (wrist-worn data), your evals on wrist datasets improved despite the evals on other datasets decreased. This is telling me that realistic data is still better than synthetic data if available. I wouldn't expect a model pre-trained on the wrist to work well with other placements, for example. It may be worth discussing the value of realistic and synthetic data in different use cases. Given this observation, I was surprised to find that your BioBankSSL experiments showed that synthetic pre-training on wrist data did better. Overall, human activity recognition has poor benchmark datasets, often extremely limited in scale (n<50 participants). I wouldn't hold the authors accountable for addressing the benchmark issue for the community. It will take time, but once this paper is accepted and externally validated by other researchers, we can better test the generability of the proposed framework. You have done a great job addressing most of my concerns, and I have adjusted my score from 6->7. Obviously, it would be great if you could reply to my new queries above :D
Dear Reviewer, Thank you so much for your insightful comments and for raising the score! We have answered your questions as follows. [1] Few-shot fine-tuning performance For datasets with a public train/test split (e.g., UCI-HAR), we adopt the available divisions. For the remaining datasets, we split them based on subjects, except for only very few datasets where we didn’t find subject specification (e.g., UT-Complex). Our test window size is 10 seconds, which carries sufficient information for the model to infer the activities and to minimize bias from random noise. UniMTS shows great generalizability in few-shot scenarios due to its pre-training on our synthesized data with diverse device locations, orientations and activity types. In contrast, without such pre-training on diverse device latent factors and text semantics aligning, Yuan et al. 2024 initially performs near-randomly (e.g., achieving an F1 score of 25 for 4-class Opportunity dataset). However, self-supervised pre-training on large-scale BioBank data also provides good initialization for Yuan et al. 2024 to converge faster, so the model is able to quickly improve its performance as more samples per class are given. [2] Discussion on synthetic and real-world data Yes, we believe that the community would greatly benefit from large-scale real-world data with comprehensive joint coverage and detailed text descriptions, if such a dataset becomes available in the future. Should such datasets become available, our framework of graph encoding and contrastive semantic aligning is still directly applicable to model these new datasets, and incorporating these data into our pre-training framework will potentially lead to more significant improvements. However, in the absence of such comprehensive real-world data, currently synthetic data still present higher quality than real-world datasets with limited joint coverage and limited text descriptions. Therefore, we generate synthetic data as the first step to address the challenges, establishing the first pipeline for pre-training motion time series. We believe our approach will bring insights to the community to address this long-standing challenge of data generalization, and we will make sure to discuss these comparisons between synthetic and real-world data in the final version. For the full-shot experiments of BioBank SSL, we observe that datasets with wrist-worn devices, such as WISDM and Wharf, also show slightly better performance compared to UniMTS, which is consistent with the observation that incorporating Capture-24 slightly improves performance on these wrist-worn datasets. For few-shot experiments, given that BioBank SSL is not pre-trained to align with text semantics, it would be hard for it to generalize to new activities given few shots, even for datasets with wrist-worn devices. Therefore, UniMTS consistently outperforms BioBank SSL in the few-shot scenarios, demonstrating the significance of our pre-training pipeline. Thank you once again for your invaluable feedback!
Thank you very much for the helpful feedback. Your evals make sense. I fear that the issue with existing benchmark is that it is very easy to overfit on tiny test set (data with a few subjects) with a large model. So I wouldn't necessarily trust the test results even though they conform with existing literature for consistency sake. This work is of high quality and has high impact to the field of ubiquitous computing and moderate impact on several other fields including pre-training using synthetic data and multi-modal learning. I thoroughly enjoyed reading this paper. Looking forward to seeing the final version this manuscript and the open release of the model!
Thank you so much for your insightful comments and for recognizing that our work is of high quality and high impact! We have tried our best to evaluate the model on a diverse set of real-world datasets, and to reduce bias by splitting train/test data based on subjects. We believe that future availability of larger-scale benchmarks will further validate the robustness of our model. We greatly appreciate your interest in our work and are committed to releasing the model upon acceptance. Thank you once again for all your invaluable feedback!
Summary
In the context of time series tasks, the paper addresses motion time series data collected from mobile and wearable devices such as smartphones and smartwatches. However, training and testing on the same dataset leads to poor generalizability of the models. The authors propose UniMTS, a unified pre-training procedure for motion time series that generalizes across diverse device latent factors and activities.
Strengths
- The paper is well written and easily understandable. - The soundness of the technical claims are good and the experiments support the proposed method. - The problem setup is well motivated. The addressed problem exists in many application areas and research fields. - The paper proposed a large experiments on many different datasets showing a higher performance than state-of-the-art methods.
Weaknesses
- After reading the Abstract, the actual contribution of the paper is unclear. The Abstract describes several minor contributions, but what is the focus of the paper? - It is unclear which large language model is used (should be mentioned earlier). - Does the proposed method actually fit to NIPS? The contributions are limited/unclear.
Questions
- Why cannot domain adaptation be utilized to address the shift in data or continual learning to adapt to new activities, etc.?
Rating
7
Confidence
3
Soundness
4
Presentation
4
Contribution
3
Limitations
- The limitation is that the main contribution of the paper is utilizing LLMs to improve the generalizability of time series models.
Summary
This paper introduces a unified pre-training procedure for motion time series that generalizes across diverse device latent factors and activities. A contrastive learning framework that aligns motion time series with text descriptions enriched by large language models and the spatio-temporal graph networks are utilized to capture the relationships across joints for generalization across different device locations. Experimental results show the superior performance of the proposed method that selected baselines.
Strengths
1. The paper is well-written and easy to follow. 2. The paper propose a unified pre-training framework for motion time series based on contrastive learning. Specifically, the proposed framework introduce LLM to incorporate the knowledge of text descriptions into time series modeling. 3. Extensive experiments demonstrate the effectiveness of the proposed method.
Weaknesses
1. It would be better to provide more descriptions regarding the usage of LLM. 2. It would be better to discuss the space and time complexities of the proposed framework. 3. As efficiency is important in real-world applications. It would suggest conducting experiments to compare the proposed method's training time (including pre-training time) with baselines.
Questions
1. What is the motivation for the usage of LLM? 2. Can the proposed method improve the efficiency of existing methods (e.g., baselines)? 3. How do you obtain the text descriptions?
Rating
7
Confidence
5
Soundness
3
Presentation
3
Contribution
3
Limitations
Please see the weaknesses and questions.
We sincerely thank the reviewer for the insightful feedback. We compare the theoretical space and time complexities with the best-performing baseline ImageBind as follows. Since UniMTS and ImageBind share similar text encoders, our analysis mostly focuses on their respective IMU encoders. Let $B$, $L$, and $F$ represent the batch size, sequence length, and feature dimension, respectively. Additionally, let $V$ and $V'$ denote the total number of joints (with $V=22$ for the HumanML3D dataset) and the number of joints with attached devices in downstream datasets ($V' \leq V$). We note that the skeleton graph is sparse and the number of edges is $E=V-1$. For UniMTS, the time complexity is $\mathcal{O}(BVLF^2 + BVLF)$ and the space complexity is $\mathcal{O}(BVLF + F^2 + V)$. For ImageBind, the time complexity is $\mathcal{O}(BV'LF^2 + BV' L^2F)$ and the space complexity is $\mathcal{O}(BV'LF + F^2 + BV'L^2)$. We observe that UniMTS does not contain the $L^2$ terms present in ImageBind (i.e., complexity grows linearly rather than quadratically with respect to sequence length), making it more efficient in terms of both time and space complexities.
Summary
This paper presents a novel pretraining method for IMU time series (i.e., acceleration + angular velocity). The authors make use of human skeleton sequences and simulate IMU measurements by calculating the acceleration and angular speed at each joint. These simulated data are then used to pre-train a spatial-temporal graph encoder. Since each skeleton is paired with texts, the graph encoder is trained with contrastive learning against CLIP text features. Augmentations on IMU data and texts are performed to boost the performance.
Strengths
1. The idea of generating IMU measurements from skeleton sequences is very interesting. On one hand, it brings a huge amount of plausible motion time series for pre-training. On the other hand, it could draw connections between IMU and other modalities. 2. The authors conduct extensive experiments and the proposed method consistently outperforms other baselines in both zero-shot, few-shot, and full-shot settings. The results look promising to me.
Weaknesses
1. I have some concerns about the quality of the generated IMU data as it is constrained to 20Hz due to the original skeleton sequences. This sampling frequency might be enough for action recognition but it is unknown whether it can be used for other tasks such as inertial navigation. Besides, some sensor-level distortion such as IMU drift or some spike noises cannot be simulated using the proposed method. 2. What is the model size of the graph encoder? Since the model is trained on IMUs from many joints (>20?), the model might be huge when fine-tuned on a dataset that has fewer IMU sensors (e.g., 2 sensors), which could make the model inefficient for real-world applications.
Questions
Please refer to weakness
Rating
6
Confidence
3
Soundness
3
Presentation
3
Contribution
3
Limitations
The authors have adequately addressed the limitations
Summary
The paper introduces a new pre-training approach designed specifically for motion time series data from mobile and wearable devices. The proposed method, UniMTS, employs a contrastive learning framework to align motion time series with text descriptions, enhanced by large language models. This approach addresses the challenges of device placement variation, orientation differences, and activity variability. The model is evaluated on 18 benchmark datasets and demonstrates significant improvements in zero-shot, few-shot, and full-shot settings.
Strengths
Originality: The paper presents a highly original approach by combining contrastive learning with synthetic data generation and graph networks to pre-train on motion time series data. This is a novel application of existing techniques to a new domain, addressing the unique challenges posed by motion data. Quality: The methodology is well-developed and the experiments are thorough. The authors provide a detailed description of their approach, including the use of synthetic data and graph networks. The evaluation on a wide range of datasets strengthens the validity of their findings. Clarity: The paper is clearly written and well-organized. Each section logically follows from the previous one, making the complex methodology easy to understand. The appropriate use of figures and tables helps illustrate key points and results effectively. Significance: UniMTS addresses a critical gap in the field of motion time series analysis. By improving generalization across different device placements, orientations, and activities, this method has the potential to significantly impact applications in healthcare, sports, and IoT. The impressive performance improvements demonstrated in the experiments underscore the method's potential.
Weaknesses
Evaluation Comparisons: The paper could be strengthened by comparing the proposed method with more recent benchmarks such as Ego4D and Exo. This would provide a clearer picture of how UniMTS stands relative to the current state-of-the-art methods in the field. Generalizability of Results: While the method shows strong results across multiple datasets, there is limited discussion on the potential limitations or specific conditions where the model might underperform. A more detailed analysis of the generalizability and potential edge cases would be beneficial.
Questions
Can you provide a comparison with the Ego4D and Exo benchmarks?
Rating
7
Confidence
4
Soundness
3
Presentation
3
Contribution
3
Limitations
None
Some concerns are addressed
I would like to thank the authors for providing detailed feedback, which addressed some of my concerns. First, the additional experiments are valuable. Results in both Table 1 and Table 2 provide interesting insights about the synthetic data. Second, the additional related works are convincing, I believe these studies will benefit this paper and put this paper in a better position. Third, I hope some explanations could be incorporated in the final version, e.g., the same rotation for gyro and acc. However, I still have one concern about the zero-shot setting in 7 and 8. If methods like ImageBind are only trained with unsupervised pre-training, it will not have the knowledge to connect motion data with corresponding labels. Or, the zero-shot means ImageBind is also fine-tuned with labels (such as walking and running), but zero-shot for recognizing new activities (such as still and biking)? Another concern is the practical impact. Given the low performance of recognizing new activities, I doubt how such performance could generate a practical impact for real-world usage. Again, overall this is a good paper with a good presentation, clear motivation, and high potential for a broad impact. Great work.
We sincerely thank the reviewer for the insightful comments, and for recognizing that our paper has good presentation, clear motivation, and high potential for a broad impact. We have addressed the concerns as follows. [1] References and explanations We will make sure to include the additional related works, as well as detailed explanations such as the same rotation for gyro and acc. [2] Zero-shot setting ImageBind is pre-trained to use image modality as a common bridge to bind multiple modalities together, including motion time series and text. More specifically, the model is pre-trained to align pairwise images and motion time series from the Ego4D daily-life activity dataset, as well as to align large-scale web images and text. By having image modality as the common bridge, the model therefore has the knowledge to connect motion time series with text descriptions. However, as Ego4D only contains motion time series from head-mounted devices, the model struggles to generalize to downstream datasets with different device placements. [3] Practical impact We would like to note that recognizing new classes in a zero-shot fashion is extremely challenging. Despite the difficulties, UniMTS has outperformed the best existing baseline by an impressive 342.3%. Furthermore, the top-2 retrieval score has increased to approximately 60. In practice, it is often feasible to obtain a few labels for downstream datasets. With just one labeled example per class, the F1 score can improve to about 60, and it further enhances to about 75 with ten labeled examples per class, which significantly outperforms the best baseline by 16.3%. Additionally, even in the full-shot setting, UniMTS continues to outperform the best baseline by 9.2%, making it practically impactful for a broad range of real-world use cases. Thank you once again for all your insightful feedback!
Dear authors, Thank you for your timely response! While I agree with you on some points, some concerns remain. 1.The pairwise alignment makes sense to me. However, there are still labels needed to enable the model to know, what kind of time series looks like a specific label, say, "walking". Then, in future cases, when the model is given a time series of new labels, say, "running", the model can make the prediction. Therefore, in terms of zero-shot, I guess you mean the model has seen "zero" training samples of data from new categories but still needs some labeled data from at least some labels. Hopefully I have made myself clear. 2.Your responses make sense to me and I admit the improvement compared to SoAs is significant enough. I still think there is some steps to go to make it practical, at least in the zero-shot setting. But this is totally fine. Given the comprehensive rebuttal and most of my concerns are addressed, I have raised my score. Hope to see the camera-ready version, great work!
We sincerely thank the reviewer for the insightful feedback and for supporting our work. We have addressed the concerns as below. [1] We follow the same zero-shot setting as CLIP [1] for both UniMTS and ImageBind. During pre-training, the model learns to connect motion time series with text descriptions on the pre-training dataset. During evaluation, the model predicts new labels on new downstream datasets not seen during training in a zero-shot manner. The term zero-shot refers to the model’s exposure to both new downstream datasets as well as new categories without prior direct training on them. [2] Yes, the zero-shot setting is extremely challenging. Apart from the 342.3% improvement over the best existing baseline, we believe that with the availability of high-quality, large-scale real-world datasets in the future, our framework can be directly applied to model these datasets during pre-training and achieve even more significant improvements. Thank you once again for your support of our work! [1] Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J. and Krueger, G., Learning transferable visual models from natural language supervision, ICML 2021
Thank you for the detailed responses. As you mentioned, the model is only exposed to old datasets and old categories for the training process, which makes sense to me. I am also interested in the new category prediction. Is the model capable of predicting any new categories? Or, you need to pre-define a few new categories (e.g., running and jogging), so the model can link the representation with the pre-defined new categories.
We sincerely thank the reviewer for the insightful comments. Our text encoder leverages pre-trained weights from CLIP, which inherently understands semantic similarities between words. During pre-training, we didn’t pre-define any categories; we fed the model with extensive text sentence descriptions to align motion time series with CLIP’s semantic space. Some example text descriptions in our pre-training dataset are “the standing person kicks with their left foot before going back to their original stance”, “a man lifts something on his left and places it down on his right”, “a person walks slowly forward then toward the left hand side and stands facing that direction”. During evaluation, UniMTS leverages such semantic awareness to effectively generalize to new activities. For example, during pre-training the model has encountered “walking” and “running”, during evaluation it can leverage the knowledge that “jogging” represents a movement with intensity level in between “walking” and “running” to successfully generalize and recognize “jogging”. Other examples include generalizing from “ascending stairs” to “walking on inclined treadmill”, and from “waving hands” to “drawing circle counter clockwise”. We sincerely thank the reviewer once again for the invaluable feedback and support.
Thanks to the authors for answering my questions. I am satisfied with the responses except for the Space and time complexities compared with the baselines. I mean the theoretical space and time complexities analysis. Thanks.
Response to the authors
I would like to thank the authors for their responses. Most of my concerns have been resolved. It is a good work with potential broad impact. Look forward to the release of dataset and pre-trained models.
We sincerely thank the reviewer for all the insightful feedback, and for the recognition of the quality and potential broad impact of our work. We are committed to releasing the datasets and pre-trained models upon acceptance of the paper. Thank you once again for your valuable feedback and support.
We sincerely thank the reviewer for all the insightful feedback and we greatly appreciate your support!
Thank you for your comprehensive response and the further clarifications provided. The paper's contribution is now well understood. The primary contribution lies in the innovative pre-training framework, which enables generalization across datasets with varying device locations, orientations, and different activity types. The experiments presented substantiate this contribution. I also appreciate the additional references provided. All my concerns have been addressed, and I have increased my rating to acceptance, recognizing the potential for broad impact.
We sincerely thank the reviewer for all the valuable feedback and for recognizing the potential broad impact of our work. We greatly appreciate your support!
I want to thank the authors for their thorough response and new experiments.
We sincerely thank the reviewer for all the insightful feedback and for supporting our work!
Decision
Accept (poster)