Automated Gait Detection in Older Adults during Daily-Living using Self-Supervised Learning of Wrist- Worn Accelerometer Data: Development and Validation of ElderNet

doi:10.21203/rs.3.rs-4102403/v1

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Automated Gait Detection in Older Adults during Daily-Living using Self-Supervised Learning of Wrist- Worn Accelerometer Data: Development and Validation of ElderNet

https://doi.org/10.21203/rs.3.rs-4102403/v1

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published 06 Sep, 2024

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Progressive gait impairment is common in aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data. Validation involved two diverse cohorts, including over 1,000 participants without gait labels, as well as 83 participants with labeled data: older adults with Parkinson's disease, proximal femoral fracture, chronic obstructive pulmonary disease, congestive heart failure, and healthy adults. ElderNet presented high accuracy (96.43 ± 2.27), specificity (98.87 ± 2.15), recall (82.32 ± 11.37), precision (86.69 ± 17.61), and F1 score (82.92 ± 13.39). The suggested method yielded superior performance compared to two state-of-the-art gait detection algorithms, with improved accuracy and F1 score (p < 0.05). In an initial evaluation of construct validity, ElderNet identified differences in estimated daily walking durations across cohorts with different clinical characteristics, such as mobility disability (p < 0.001) and parkinsonism (p < 0.001). The proposed self-supervised gait detection method has the potential to serve as a valuable tool for remote phenotyping of gait function during daily living in aging adults.

Health sciences/Neurology/Neurological disorders

Health sciences/Health care/Geriatrics

Physical sciences/Engineering/Biomedical engineering

Aging is associated with progressive loss of motor function. These deficits are heterogeneous and may manifest as reduced walking speed, poor balance, increased, gait variability, increased fear of falling, and shorter stride length^1–3. Objective measures of gait obtained during brief supervised gait testing in a lab or clinic predict survival, varied adverse health outcomes, and loss of independent living^4–6. However, these brief assessments provide only a limited snapshot of an individual's gait abilities and may not reflect function and variability during the manifold demands of daily living^7,8. Advances in unobtrusive sensor technology afford investigators the opportunity to obtain a more comprehensive assessment of mobility via remote multi-day recordings of daily living. However, the automated analytic tools employed for many commercially available devices focus nearly exclusively on healthy younger adults and do not account for the impairments observed in older adults during device development or validation^9,10. Hence, there is an urgent need for the development and validation of automated tools to quantify daily living gait among the full health spectrum of older adults that reside in the community-setting^11,12.

Previous studies investigating real-world gait employed accelerometers worn on the lower back, leveraging the inherent quasi-periodicity of lumbar movement during walking¹³. While these studies have demonstrated the potential of assessing daily living gait, sensor placement on the lower back may present limitations for long-term adherence due to potential discomfort¹⁴. A different approach is to ask participants to wear a wrist-worn accelerometer. Wrist-worn accelerometers have gained widespread use to measure daily living physical activity ^15–18. In this regard, the ubiquity and popularity of smartwatches make wrist-worn accelerometers a practical choice for ensuring high compliance in daily living studies. Wrist-worn accelerometers enable the extraction of a wide range of daily living behaviors, including sleep patterns¹⁹, circadian metrics²⁰, and levels of physical activity²¹. While estimated physical activity levels can provide many insights^22,23, to date, most studies using a wrist-worn accelerometer lacked detailed and high-resolution information about other crucial facets of gait quality²¹. Therefore, recent efforts have focused on leveraging these accelerometers to assess walking and gait quality.

The first step in deriving gait metrics from an accelerometer is the detection of gait sequences from the raw accelerometer signals^24,25. Gait detection from a wrist-worn accelerometer is more challenging compared to other locations, such as lower limbs or lower back, due to the non-gait related hand movement and the fact that wrist movements often deviate from the expected periodic swinging during the gait cycle. This may occur, for instance, when an individual walks while simultaneously engaging in other activities, such as texting. This challenge is exacerbated for older adults and people with gait disturbances, such as Parkinson’s disease who manifest reduced arm swing during walking²⁶. People with Parkinson’s disease also exhibit symptoms of tremor and dyskinesia, which impact wrist movements and contribute to an overall less stable and consistent gait pattern, factors complicating gait detection algorithms²⁴.

Classical gait detection algorithms employ signal processing techniques, such as peak detection and wavelet analysis, to extract features both from the time and frequency domain^25,27. These features are then used to identify gait sequences based on the repeated periodic waveforms manifested during gait. However, the complex wrist movements render the differentiation between gait and non-gait movements very challenging. Alternative approaches are needed to detect gait from wrist-worn accelerometers.

Previous studies addressed this goal by employing supervised machine learning algorithms that were trained to identify patterns in the signal associated with gait^17,28,29. Kluge et al.²⁵ conducted a comprehensive analysis of gait detection algorithms using accelerometer data from lower-back and wrist-worn accelerometers. The algorithms were trained on data from healthy young adults and subsequently tested on diverse subsets of adults from the Mobilise-D technical validation study³⁰, including older adults with and without varied diagnoses. They found, not surprisingly, that algorithms based on lower-back data outperformed wrist-based algorithms. Yet, the reduced performance of wrist-based algorithms may be attributed, in part, to being trained on data from healthy young adults, potentially leading to suboptimal performance among older adults. This highlights the need to optimize wrist-based algorithms for older adults, who more commonly show heterogeneous gait abnormalities that do not occur as frequently in younger adults.

The best performing wrist-based algorithm identified in the study by Kluge et al. was initially developed and validated in Brand et al.²⁴. In this study, we employed a supervised convolutional neural network with U-Net architecture³¹ for gait detection, focusing on older adults and people with Parkinson’s disease (PD). The results were then compared to those of a control group comprising healthy young adults. Our findings indicated that biological meaningful measures of gait quality (e.g., cadence and gait regularity) and quantity (e.g. daily walking duration) could be derived from a wrist-worn accelerometer. However, it is crucial to note that the model's performance was reduced when applied to older adults and individuals with PD, compared to the healthy young adult control group. An important impediment for training a supervised model that can be applied to older adults and varied clinical conditions derives from the scarcity of ground-truth labels indicating the temporal location of the gait sequences, especially for recordings of unsupervised movement during daily living.

Recently, there has been a growing interest in leveraging self-supervised learning (SSL) methods to overcome the gap imposed by the shortage of labeled data³². SSL generally comprises two main stages. First, learning feature representations of varied signals using a substantial amount of unlabeled data, which can be achieved through methods such as multi-task learning (MTL)³³ and contrastive learning^32,34. An example of contrastive learning is the SimCLR method: “A Simple Framework for Contrastive Learning of Visual Representations”³². In these approaches, the model's objective is to predict characteristics of the signal that do not require any labels. This stage is commonly referred to as the 'pretext' stage. The second stage involves fine-tuning the SSL model with a smaller set of labeled data in a supervised manner for a downstream task (e.g., gait detection).

The SSL approach has demonstrated significant potential in varied human activity recognition tasks^35–37. For example, Yuan et al.³⁸ utilized the UK Biobank dataset, which comprised daily living recordings from a wrist-worn accelerometer, to develop an SSL model for activity recognition and exhibited improved performance in several tasks and datasets. Small et al.³⁹ fine-tuned this SSL model for gait detection on a semi-living dataset, termed OxWalk, which included approximately one hour of recording in a home environment. However, the dataset used for fine-tuning included only healthy adults (N = 39, mean age = 38.5 years). Thus, their model may not be optimized for older adults or individuals with gait disturbances.

Here, we developed and evaluated a gait detection deep learning approach, termed ElderNet, that was oriented and optimized for older adults and, in particular, those who might have impaired gait. The first stage involved the training of an SSL model, utilizing the pre-trained UK Biobank model of Yuan et al.³⁸. This SSL model was extensively modified in both architecture and training cohorts to include a large unlabeled dataset of more than 1000 older adults with and without impaired gait who wore a wrist-worn device for up to 10 days (mean age 83 years old) and were participating in the RUSH Memory and Aging Project (MAP)^40–42. Next, we fine-tuned the model on a labeled dataset consisting of 83 older adults (mean age = 71.9 years) from the Mobilise-D technical validation study³⁰, each wearing a wrist-worn accelerometer for approximately 2.5 hours. The Mobilise-D dataset is one of the largest available labeled datasets that include daily living recordings from a wrist-worn accelerometer in older adults. It contains a ground-truth reference for indicating the presence or absence of gait sequences. Additionally, the dataset contains participants with different health conditions, presenting a range of gait patterns, including individuals with Parkinson's disease, proximal femoral fracture, chronic obstructive pulmonary disease, congestive heart failure, and healthy adults. To explore the added value of the putative enhancements of the ElderNet, we compared it to two state-of-the-art algorithms: the U-Net architecture, which achieved the highest results in the study by Kluge et al.²⁵, and the model developed by Small et al., termed OxWalk, utilizing the strong UK Biobank SSL model.

Finally, we applied ElderNet to a set of new participants – not previously trained by the model - to begin to explore its construct validity and generalizability. Construct validity refers to the degree to which a measurement tool, like ElderNet, accurately evaluates its intended purpose, specifically gait detection. In this context, we examined walking duration obtained through ElderNet across cohorts whose clinical status is likely to lead to reduced daily living walking.

Performance of the Gait Detection Algorithm

To develop ElderNet, an SSL model was trained using the MAP database constituting 950 participants. Next, the labeled data from Mobilise-D was used for fine-tuning ElderNet and evaluating its performance (Fig. 1). 83 participants were included in the Mobilise-D dataset. Table 1 summarizes the characteristics of the Mobilise-D dataset.

Table 1

Characteristics of Older Adults in the Mobilise-D Technical Validation Study
	HA	CHF	COPD	PD	PFF
No. of Participants (N)	20	11	17	19	16
Age (years)	71.7 ± 5.8	69.1 ± 11.7	69.4 ± 9.1	69.3 ± 7.0	79.9 ± 8.2
Gender (M:F)	11:9	7:4	9:8	15:4	7:9
No. of Participants with a walking aid (and percentage)	1 (5%)	2 (18%)	1 (6%)	6 (32%)	10 (63%)
Montreal Cognitive Assessment (0–30)	27.7 ± 2.6	27.1 ± 2.9	24.6 ± 3.3	24.7 ± 3.9	23.8 ± 4.3
No. of Gait Sequences per Recording	66.5 ± 27.7	54.9 ± 31.9	60.8 ± 25.5	35.7 ± 23.6	34.0 ± 23.8
Average Length of Gait Sequences (s)	30.7 ± 25.2	29.6 ± 18.0	17.6 ± 5.0	32.6 ± 22.0	29.1 ± 16.4
Gait Percent (%) per Recording	18.0 ± 9.3	16.3 ± 17.2	10.4 ± 3.5	14.3 ± 11.2	11.5 ± 9.4

HA: Healthy adults, CHF: Congestive heart failure, COPD: Chronic obstructive pulmonary disease, PD: Parkinson’s disease, PFF: Proximal femoral fracture.

The model predictions made by ElderNet significantly outperformed the two other state-of-the-art algorithms^24,39 both in terms of accuracy and F1 score. The median accuracy for ElderNet was 96.86%, surpassing the U-Net at 93.69%, and OxWalk at 92.83% (p < 0.001). In terms of F1 scores, ElderNet achieved a score of 86.52%, outperforming the U-Net and OxWalk models which achieved scores of 67.29% (p = 0.046) and 73.51% (p < 0.01), respectively (Fig. 2). Figure 3 shows a representative example of a raw acceleration signal containing gait sequences, along with the predictions of the different models and the corresponding ground-truth labels.

Exploring Construct Validity

To examine the construct validity of the output of ElderNet, we first applied it on an unseen portion of the MAP dataset (N = 157) that was not utilized during the training of ElderNet. Table 2 summarizes the characteristics of this test dataset. A preliminary analysis based on the detected gait events, revealed a few statistically significant differences across different subject populations and disease cohorts. The average daily walking duration displayed variations among participants in different demographic and clinical groups, as demonstrated in Fig. 4. A significant difference in daily walking durations was observed between age groups, indicating a decline in walking activity with age, supporting its utility. To account for this, we performed partial correlation analyses, adjusting for age, sex, and BMI in subsequent comparisons, and found that the differences between groups remained statistically significant.

Table 2

Average Daily Walking Durations across Demographic and Clinical Factors in the MAP unseen Dataset.
Characteristic		N (%)
Overall		157 (100)
Age (years)
	60–69	4 (2.55)
	70–79	33 (21.02)
	80–89	78 (49.68)
	90–99	40 (25.48)
	100+	32 (1.27)
Sex
	Female	126 (80.25)
	Male	31 (19.75)
BMI (kg/m²)
	Underweight (< 18.5)	5 (3.18)
	Normal Weight (18.5–24.9)	46 (29.30)
	Overweight (25-29.9)	60 (38.22)
	Obese ( > = 30)	40 (25.48)
	Unknown	6 (3.82)
Mobility Disability Score*	0	74 (47.13)
	1	28 (17.83)
	2	28 (17.83)
	3	24 (15.29)
	Unknown	3 (1.92)
Falls in the past year	0 falls	106 (67.52)
	>=1 falls	49 (31.21)
	Unknown	2 (1.27)
Parkinsonism	No parkinsonism	37 (23.57)
	Possible parkinsonism	35 (22.30)
	Parkinsonism	39 (24.84)
	Unknown	46 (29.30)

*0 indicates the absence of mobility disability. 3 indicates a high level of disability.

Furthermore, Fig. 4 illustrates that participants with a mobility disability score of 0 (no mobility disability) walked significantly more minutes per day than those with scores of 2 (p < 0.01) and 3 (p < 0.01). Additionally, participants with a mobility disability score of 1 also showed a significant difference from those with a score of 3 (p < 0.048). Examining participants with different parkinsonism scores in terms of the number of parkinsonian signs, we observed that individuals without any parkinsonian signs walked significantly more than those with 1 sign (p < 0.001) or two or more signs (p < 0.001).

In this work, we developed and validated a gait detection algorithm (ElderNet), specifically designed for older adults with and without gait impairments. ElderNet demonstrated superior performance compared to the two state-of-the-art models. It achieved the highest accuracy, significantly surpassing the OxWalk model³⁹. Moreover, its F1 score was higher than both OxWalk and the U-Net²⁴ models. Additionally, ElderNet achieved at least comparable results in other metrics such as specificity, recall, and precision. The imbalance between gait and non-gait sequences in daily living is often expressed by a significant trade-off between precision and recall²⁴. While the U-net and the OxWalk models indeed exhibited such a trade-off, our model was prominent with stable precision and recall, resulting in a high F1 score. This suggests that ElderNet is well-suited for daily living data, capable of identifying most existing gait sequences (i.e., high recall) with high confidence (i.e., high precision).

Gait detection algorithms often lack labeled data from daily living datasets, particularly for older adults and individuals with gait impairments. This scarcity of labeled data prevents the algorithms from being optimized for these populations, whose gait signals can be diverse and abnormal. Here, an SSL method was utilized to address this gap. First, a pre-trained model trained on the UK Biobank data was leveraged. The UKB dataset consists of 100,000 participants who wore a wrist-worn accelerometer in their daily lives, making it the largest dataset of its kind. Due to its size, we anticipated benefits from incorporating this pre-trained model into our SSL phase. Indeed, utilizing this pre-trained model led to a higher F1 score (82.59) than training the SSL model from scratch (F1 score of 77.15, Supplementary Table S1).

Our objective was to develop a gait detection algorithm tailored for older adults, aiming to bridge the current accuracy gap observed in algorithms designed for this population²⁴. While the UK Biobank dataset included a large number of older adults, its participants were recruited in the age range of 45–69, with a mean age of 62 for the visits that involved wearing the wrist accelerometer. To address this limitation, we leveraged the MAP dataset with a mean age of 83 years old (range 62–103) and more than 1000 participants. We found that integrating the MAP data into our combined model enhanced its overall performance (Supplementary Table S2). This improvement may be attributed to the fact that the extensive MAP data used to train ElderNet better represented the characteristics of the target population i.e., older adults that were also reflected in the test set Mobilise-D data.

Two different SSL approaches were explored, namely MTL and SimCLR. Overall, both methods yielded similar performance, with a slight advantage favoring the MTL results, but with no significant difference (Supplementary Table S3). These findings are consistent with a previously published paper that observed similar results for SimCLR and MTL in human activity recognition tasks using acceleration data from the wrist³⁵. Finally, we compared ElderNet with its supervised counterpart (Supplementary Table S4). Remarkably, our model exhibited superior performance compared to its supervised counterpart, achieving an F1 score of 84.74 for ElderNet compared to 79.21 for the supervised model. This underscores the potential of leveraging large unlabeled data to learn feature representations of the data.

In this study, the Mobilise-D data was utilized for the fine-tuning phase, leveraging its unique characteristics. Firstly, the dataset incorporates a robust reference system, the INDIP system, whose accuracy has been previously validated against an optical motion capture system. The results showed excellent absolute agreement (ICC > 0.95) within a laboratory setting^43,44, establishing the INDIP system as a reliable method for obtaining reference data in real-world environments. Moreover, the Mobilise-D dataset contains daily living data from older adult populations, particularly those with specific medical conditions that affect mobility. Notably, this cohort includes older adults who utilize walking aids, exhibiting abnormal gait signals from the wrist accelerometer, thereby complicating gait detection²⁵. However, ElderNet exhibited high performance on this subcohort, showcasing its generalizability to diverse gait patterns. While we acknowledge that the 2.5-hour assessment used in Mobilise-D data may not fully capture the complete variability of real-world walking, this dataset remains one of the largest available with comprehensive gait and non-gait reference information across various disease indications with labels.

The establishment of ElderNet sets the stage for subsequent studies aimed at extracting meaningful digital mobility outcomes related to gait quantity and quality from the identified gait sequences^17,45. Gait measures have already been shown to serve as potential biomarkers for age-related health outcomes^5,46. Notably, gait speed has been shown to be associated with survival rates in older adults⁴⁷. A recent study has demonstrated that using a simple model based solely on mean acceleration data can facilitate the prodromal diagnosis of Parkinson's disease⁴⁸. We hypothesize that incorporating higher-level gait measures into such models can augment their predictive capabilities, leading to better identification of multiple neurological conditions that manifest with gait impairments.

It is important to highlight that we standardized the sampling rate of all datasets to 30 Hz to align with the frequency used in the pre-trained UK Biobank model. This relatively low sampling rate allowed for the efficient use of long-duration recordings. Exploring the ramifications of using different sampling rates should be addressed in future work. While the MAP data utilized in the SSL phase and the participants from the Mobilise-D data shared similarities in their emphasis on older adults, there were notable differences between them. Particularly, the average age of the MAP is higher (83 years) than that of the Mobilise-D data (72 years). Additionally, the Mobilise-D dataset predominantly includes participants with specific medical conditions, unlike the MAP data which is not exclusively focused on populations with diseases. We attempted to address this by standardizing both datasets (MAP and Mobilise-D) using a zero-mean unit-variance whitening³⁵. However, we observed that standardizing the MAP data, but not the Mobilise-D data, resulted in improved outcomes (Supplementary Figure S1).

The data was segmented into non-overlapping 10-second windows, both in the SSL and fine-tuning steps, to align with the UK Biobank pre-trained model, which utilizes the same window size. Consequently, we defined windows containing 5 seconds or more as gait windows in our labeled dataset, omitting gait sequences shorter than five seconds. However, this approach can lead to an underestimation of the number of gait sequences that occur in daily living. A potential consequence of this approach could be the estimated daily walking duration, as observed in the construct validity step (recall Fig. 4), which was found to be slightly lower than reported in the literature⁴⁹. To address this issue, we explored the use of dense labeling, involving a shift to per-sample labels and outputs in the fine-tuning model. Despite this modification, the model's performance was found to be lower compared to using window-based labeling, and there was no meaningful change observed in the estimated daily walking time (Supplementary Table S5, Supplementary Figure S2). This suggests that the alternative dense labeling strategy does not provide a significant improvement in capturing daily walking patterns.

This study introduced ElderNet, a novel gait detection model developed and validated for older adults with and without known health conditions that can affect gait. The model demonstrated high performance in accurately identifying real-world gait sequences extracted from wrist recordings. When applied to unlabeled daily living data, ElderNet successfully revealed differences between different clinical groups supporting further clinical testing of its efficacy. Given that many older adults experience gait impairments, a reliable system for gait quantification is crucial for obtaining a comprehensive characterization of gait function remotely during daily living. ElderNet addresses that need.

This study was composed of four stages:

Self-supervised learning: training an SSL model on a large amount of unlabeled activity data to learn the feature representation of daily living acceleration data.
Fine-tuning: utilizing the model from the SSL step for training a supervised gait detection system (ElderNet) using labeled data.
Gait Detection Test Phase: comparing the results of the gait detection model with 2 state-of-the-art algorithms on an independent test set.
Exploring construct validity: applying ElderNet on another unseen dataset to examine the potential of gait-based analysis for identifying differences between cohorts of different clinical characteristics.

Preprocessing

To maintain uniformity in comparison with state-of-the-art algorithms, we standardized the acceleration data across the various cohorts by resampling to a 30 Hz resolution and dividing the signals into 10-second non-overlap windows, following a methodology similar to the UK Biobank study^38,39. We considered the window as a gait window only when half or more of it was labeled as gait. Given that the typical gait frequency is less than 10 Hz, the 30 Hz sampling rate surpasses the Nyquist frequency, preventing any loss of essential signal information.

Stage 1: Self-Supervised Learning

Participants and Wearable Sensors

Participants were community-dwelling older adults enrolled in an ongoing cohort study of chronic conditions of aging, known as Rush Memory and Aging Project^40–42. A total of 1117 participants aged between 61 and 103 years (mean 83.77 ± 7.37 SD) (76% female) participated in the study. The dataset was divided into two sets: 85% of the data (n = 950, mean age = 83.6 ± 7.3 years, 76% female) was utilized for the SSL model training, while the remaining 15% (n = 167, mean age = 84.2 ± 7.6 years, 80% female) was reserved for construct validity step (see the construct validity section). Written informed consent was obtained, and the study was conducted by the latest version of the Declaration of Helsinki and was approved by Rush University Medical Center Institutional Review Board.

Participants wore the GENEActiv device (Activinsights Ltd.; Cambridgeshire, UK), a triaxial accelerometer, on their non-dominant wrist for 24 hours/day for up to ten consecutive days. Acceleration data were sampled at 40/60 Hz, with a range of ± 8 gravitational acceleration units (g). This dataset lacks labels indicating the presence or absence of gait. The free-living nature of this data enables us to expose the SSL model to a variety of human activities and gait sequences, supporting the extraction of meaningful signal features.

Self-Supervised Approaches

Typically, SSL models consist of a main trunk, usually a convolutional neural network, referred to as a feature extractor, which produces a vector containing feature representations. The feature vector is then adjusted to a different dimension to match the ‘pretext’ task associated with the chosen SSL approach. In this study, we investigated two SSL approaches, namely MTL and contrastive learning (SimCLR). We selected these approaches based on their demonstrated superior performance in downstream human activity recognition tasks, as identified through an extensive exploration of various SSL approaches using wearable sensors³⁵.

In the MTL approach, each acceleration window undergoes data augmentation, where the objective of the model is to predict the augmentation of the signal (pretext task). Following the methodology of Yuan et al.³⁸, 4 distinct augmentations were employed: 1. Reversing the signal. 2. Permutation of different segments of the window, with each segment comprising 10 samples. 3. Time warping, which alters arbitrary segments of the signal by stretching and compressing them. 4. Scaling each of the acceleration axes with a random factor. Each window has a random probability of undergoing each of the augmentations, and the model predicts whether the window underwent the augmentation, resulting in four binary outputs. The model’s loss is calculated using the cross-entropy function for all four augmentations and then averaged to produce the final loss.

The SimCLR contrastive learning method also employs data augmentations. In SimCLR, each window undergoes two augmentations, resulting in two distinct views of the same window. Views originating from the same source window are considered “positive” pairs, while views stemming from different sources are considered “negative” pairs. For instance, if we initially have N windows of acceleration signal, the transformation yields 2N views of the windows. Thus, for every positive pair of windows, there are 2N-2 negatives. In this study, we utilized a 3D rotation transformation as the augmentation function. In this augmentation, a random axis in 3D and a random rotation angle are drawn from a uniform distribution, and the corresponding rotation is applied to the window. This can be considered as a way to simulate different sensor placements³⁴, making it especially effective for wrist accelerometers where the axis orientation frequently changes. We specifically chose this augmentation due to its demonstrated superior performance in downstream human activity recognition tasks associated with the SimCLR approach³⁴. The different views of the windows pass through the model encoder (i.e., feature extractor), resulting in an output that reflects the different windows as feature vectors. Next, a contrastive loss function is employed to calculate the relationships between pairs of vectors using cosine similarity. The objective of the loss function is to maintain proximity in the feature space for vector representations of "positive pairs" while ensuring that "negative" pairs remain distant in this space. This loss is also known as the normalized temperature-scaled cross-entropy loss (NT-Xent)³².

Model Configurations

To enhance the model's performance, the incorporation of a pre-trained model as the feature extractor of the SSL model was used. Specifically, we employed a model developed by Yuan et al.³⁸, which utilized the diverse UK Biobank dataset to train an SSL model using the MTL approach. The architecture of the pre-trained model was ResNet-V2 with 18 layers. The input acceleration data underwent through the pre-trained model, resulting in an intermediate output- a vector with dimensions (1024, 1). Subsequently, we introduced additional layers on top of the pre-trained model, referred to as a model's head. The intermediate vector then traversed through these additional layers to produce the final output, suitable for the pretext task. While the weights of the pre-trained model were frozen during the training of our model, indicating they were not updated during gradient calculations, the weights of the model’s head were updated. This modification to the pre-trained model allowed us to tailor our model to older adults using the MAP data, considering that the pre-trained UK Biobank model did not exclusively focus on older adults. We termed our combined model ElderNet. Figure 1 illustrates the pipeline of our model.

We experimented with 3 different versions for the model's head, each with increasing complexity: 1. Using 3 fully-connected layers without non-linearity between them. 2. Using the same fully-connected layers, but with ReLU non-linear activation function between the layers. 3. Utilizing the U-Net with an architecture similar to the model employed during the testing phase. Supplementary Table S6 provides more details on the models' hyperparameters and implementation.

Stage 2: Fine-Tuning

Participants and Wearable Sensors

For optimizing and evaluating algorithms for gait detection, a dataset from the Mobilise-D technical validation study was used. This multi-center observational dataset, originally aimed at validating real-world digital mobility outcomes included different patient and healthy populations. A comprehensive description of the study's experimental protocol, incorporating all inclusion and exclusion criteria, can be found in³⁰. Briefly, 112 participants across five different disease cohorts and one cohort of healthy adults were studied. The patient groups included chronic obstructive pulmonary disease, Parkinson's disease, multiple sclerosis, proximal femoral fracture, and congestive heart failure patients. We excluded the multiple sclerosis group (N = 20, mean age = 48.7 years) as we aimed to customize the model to older adults and the MS cohort comprises also young adults. In addition, nine participants were also excluded due to missing data, resulting in 83 participants overall used for this step. All participants gave written informed consent before participation. The participants were monitored during 2.5 hours of real-world living undergoing their normal activities. The participants were equipped with an accelerometer worn at the wrist on the non-dominant hand and a validated multi-sensor system, the INDIP (INertial module with DIstance sensors and Pressure insoles) as reference^30,43.

Fine-Tuning Procedure

The fine-tuning step involved a supervised learning procedure. The model's input comprised the Mobilise-D dataset, which contains labels indicating the temporal location of the gait sequences. We divided the Mobilise-D data into 75%-25%, where 75% of the data was used for training and validation of the supervised model, as well as for assessing different model configurations, and the remaining 25% was reserved for testing the model. We selected this ratio to ensure comparable distributions between the training and test sets, ensuring that each cohort has at least 3 participants in the test set. The divisions were made subject-wise, ensuring that the data points belonging to a particular subject were entirely contained within one subdivision and did not get shared across other subdivisions. We utilized the trained model from the SSL step to train a gait detection model. That is, the weights learned on the extensive unlabeled data served as a robust starting point for training a supervised gait detection model. To adapt the SSL model for gait detection, we modified its last layer to function as a linear layer producing a binary output (i.e., gait/non-gait). During the fine-tuning process, we allowed the model to update all of its weights. This decision was based on prior studies that demonstrated the preference for not freezing weights in the fine-tuning procedure^38,50.

In the fine-tuning process, we again split the training set, corresponding to 75% of the entire data, into an 80 − 20 ratio. Eighty percent of this subset was used for training and 20% for validation. We applied five-fold cross-validation on the training set, stratified by class label and grouped by participant. An early-stopping mechanism was implemented to halt training when the loss stopped decreasing for five consecutive epochs. The cross-validation process was repeated with three different seeds, representing three different divisions of the folds, to obtain more generalizable results independent of a specific order of the data. The results from the three iterations were averaged to derive final performance metrics. The fine-tuning and performance evaluation processes were implemented for all different SSL configurations (refer to the Models Configuration section), utilizing only the training set. For each unique configuration, its performance after fine-tuning the Mobilise-D data was recorded. The configuration that yielded the best results was then selected as our model for comparison and further analysis. Supplementary Figure S3 illustrates the flow of this process.

Ablation studies

To further explore the influence of different components of the SSL model on its downstream performance, several ablation studies were conducted. Initially, the impact of utilizing the pre-trained weights from the UK Biobank model was investigated³⁸. For this purpose, the same architecture of the pre-trained SSL model was evaluated (i.e., ResNet) twice- once with the pre-trained weights from the UK Biobank model initialized, and once trained from random initialization on the MAP dataset.

To assess the contribution of the MAP dataset in tailoring the model to older adults, the combined network (the pre-trained model with the newly added layers) was utilized, and its performance was evaluated with and without utilizing the MAP data. This investigation allowed us to discern whether the performance difference stemmed solely from the expansion of the pre-trained model architecture (by adding the new layers) or if the use of a dataset focused on older adults, such as the MAP data, also played a role.

Stage 3: Testing

The F1 score from the fine-tuning step was used to select the best model configuration. The choice of using the F1 score for model selection is based on the inherent imbalance of daily living data in terms of gait, where gait sequences are much less frequent than non-gait ones. In imbalanced datasets, the F1 score provides a more realistic and unbiased assessment of the model's performance²⁴. Model Performance was tested at the window level (i.e., comparing the prediction and the label of each window). The assessment was conducted at the window level, comparing predictions with the corresponding labels for each window.

Model Comparison

We compared the resulting ElderNet model with two state-of-the-art gait detection algorithms. The first comparison algorithm employed a U-Net architecture, developed and validated in our recent publication^24,25. The U-Net model was originally trained on healthy young adults. The second model in our comparative analysis was an SSL algorithm pre-trained on the UK Biobank dataset and subsequently fine-tuned for gait detection in healthy adults, which was referred to as the OxWalk dataset³⁹. We tested these 3 models on 25% of the Mobilise-D data, which was not used in the fine-tuning step. The performance metrics were calculated for each of the 21 participants in the test set, and then averaged to obtain the final performance.

Stage 4: Assessing construct validity

As a preliminary exploration of the clinical potential of the gait-detection information introduced by ElderNet, we applied the model to an unseen portion of the MAP dataset, ensuring that participants used in this step were distinct from those involved in the SSL phase. A total of 167 participants were assigned to this stage. To accurately analyze participant activity, we excluded time segments indicating participants who were not wearing the device. These non-wear periods were defined as consistent low movement (low STD) across all acceleration axes for at least 30 minutes^51,52. For each participant, we extracted data from four full (24-hour-long) days, as a recent study has shown that this duration provides reliable gait quantity measures⁵³. Ten participants were excluded, due to an insufficient amount of activity (less than 96 hours of data), resulting in a final number of 157 participants who were included in this stage. ElderNet was applied to the four days of data to identify the gait sequences. Subsequently, for each day, we summed the number of gait sequences and defined the median value as the daily walking time.

To examine the construct validity of ElderNet, differences in daily walking time among participants belonging to different clinical cohorts were investigated. Specifically, we examined 2 motor-related clinical variables: the mobility disability score, assessed using the Rosow-Breslau scale⁵⁴, and the number of parkinsonism signs⁵⁵. The modified version of the motor portion of the United Parkinson's Disease Rating Scale (UPDRS III) was used to assess the presence of four Parkinsonian signs: bradykinesia, gait, rigidity, and tremor⁵⁶. Participants were categorized into three cohorts (no sign, 1 sign, 2 + signs). We hypothesized that daily walking time would differ between these cohorts, with individuals without mobility disability spending more time walking than those with mobility disabilities²³. Additionally, we expected individuals without Parkinsonian signs to spend more time walking than those with 1 or more Parkinsonian signs⁵⁷.

Statistical analysis

The Kruskal-Wallis test was performed to identify significant differences between ElderNet and state-of-the-art models across the test performance metrics. Dunn's post-hoc analysis was applied to reveal the sources of difference among the models. In the context of construct validity, the Kruskal-Wallis test assessed differences in daily walking durations across cohorts with distinct demographic and clinical statuses. The corresponding Dunn's post-hoc analysis was then used to pinpoint the sources of variation in walking durations. To address multiple comparisons in all instances, the Bonferroni correction was applied. The Kruskal-Wallis test and Dunn's post-hoc analysis were implemented using the 'kruskal' function from the scipy.stats library and the posthoc_dunn function from the scikit_posthocs library, respectively. Partial correlation analyses were performed to adjust for age, sex, and BMI, using IBM SPSS Statistics software (Version 29.0.0.0).

Acknowledgments

The authors express their gratitude to the entire Mobilise-D Work Package 2 team for ongoing discussions and valuable insights. Special appreciation is extended to the study participants for their dedicated time and enthusiastic involvement, particularly amidst the challenges of the COVID-19 pandemic. Furthermore, heartfelt thanks go to all contributors of data from the RUSH Memory and Aging Project. The authors also acknowledge the supportive staff at the Rush Alzheimer’s Disease Center.

Author Contributions

Participant recruitment and clinical oversight: B.S, W.M, C.B, J.M.H, A.Y, L.R. Algorithm development: Y.E.B, O.P, J.M.H. Data analysis, statistical analysis: Y.E.B. Figures and tables preparation: Y.E.B. Data interpretation: Y.E.B, J.M.H, O.P. Drafting of the manuscript: Y.E.B, J.M.H, O.P. Intellectual contribution: Y.E.B, F.K, L.P, K.A, C.B, A.C, W.M, B.S, B.V, A.Y, L.R, S.DD, A.M, A.S.B, J.M.H, O.P.

All authors have provided critical intellectual input during the revision of the manuscript. All authors have reviewed the manuscript and approved the submitted version.

Data availability

Raw data of a representative participant (dataset YAR, participant 0002) can be found on Zenodo: https://doi.org/10.5281/zenodo.7185429. The full data set will be made available by the Mobilise-D consortium after June 2024.

All MAP data included in these analyses are available via the Rush Alzheimer’s Disease Center Research Resource Sharing Hub, which can be found at www.radc.rush.edu (accessed on 17 April 2023). It has descriptions of the studies and available data. Any qualified investigator can create an account and submit requests for deidentified data.

Code availability

The complete source code for the conducted analyses will be made available upon publication in a dedicated GitHub repository.

Ethics approval and consent to participate

Ethical approval was obtained at the individual sites (London-Bloomsbury Research Ethics Committee, 19/LO/1507; Helsinki Committee, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel, 0551-19TLV; ethical committee of the medical faculty of The University of Tübingen, 647/2019BO2; ethical committee of the medical faculty of Kiel University, D438/18) and all participants gave informed consent before participating.

Funding

This work was supported, in part, by grants from the NIH (R01AG017917; R01AG056352, R01AG79133, R01AT012228) and by the Mobilise-D project. The Mobilise-D project received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No. 820820. This JU receives support from the European Union's Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). Content in this publication reflects the authors’ view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.

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Competing interest reported. A. Mueller and F. Kluge are employees of and may hold stock in Novartis L. Palmerini is co-founder and own share of mHealth Technologies (https://mhealthtechnologies.it/).
C. Becker are consultants of Philipps Healthcare, Bosch Healthcare, Eli Lilly, Gait-up. S. Del Din and J. Hausdorff report consultancy activity with Hoffmann-La Roche Ltd. outside of this study.

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Automated Gait Detection in Older Adults during Daily-Living using Self-Supervised Learning of Wrist- Worn Accelerometer Data: Development and Validation of ElderNet

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Abstract

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INTRODUCTION

RESULTS

Performance of the Gait Detection Algorithm

Exploring Construct Validity

DISCUSSION

CONCLUSIONS

METHODS

Preprocessing

Stage 1: Self-Supervised Learning

Participants and Wearable Sensors

Self-Supervised Approaches

Model Configurations

Stage 2: Fine-Tuning

Participants and Wearable Sensors

Fine-Tuning Procedure

Ablation studies

Stage 3: Testing

Model Comparison

Stage 4: Assessing construct validity

Statistical analysis

DECLARATIONS

REFERENCES

Additional Declarations

Supplementary Files

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