MorphCL: Morphological Contrastive Learning for Inertial-based Human Activity Recognition

Marius Bock; Yuwei Zhang; Juergen Gall; Michael Moeller; Kristof Van Laerhoven; Cecilia Mascolo

cs.LGOct 7, 2026

MorphCL: Morphological Contrastive Learning for Inertial-based Human Activity Recognition

Authors: Marius Bock, Yuwei Zhang, Juergen Gall, Michael Moeller, Kristof Van Laerhoven, Cecilia Mascolo

Organizations: University of Bonn · University of Cambridge · Lamarr Institute for Machine Learning and Artificial Intelligence · University of Siegen

Abstract

Despite the ubiquity of sensors in wearable and mobile devices and the abundance of human movement data they generate, translating unlabeled recordings into foundational motion models remains an open challenge. Self-supervised learning (SSL) has alleviated the need for costly annotations, yet existing approaches leave the global structure of large-scale motion data largely untapped, relying on randomly sampled batches and local comparisons that become particularly problematic for in-the-wild inertial data dominated by stationary, low-variance behaviors. Here we introduce Morphological Contrastive Learning (MorphCL), a self-supervised pretraining framework that uses structure-aware grouping to inject explicit modeling of global structure into inertial-based SSL approaches. Building on two well-established pillars of motion analysis, the discovery of motion primitives, or motifs, and domain-specific feature descriptors, we show that MorphCL substantially improves linear probing and finetuning results of learned encoders by up to 15 percentage points in F1-score. In a comparison with existing foundation models, we demonstrate that MorphCL-pretrained encoders match or surpass them models in linear probing performance while trained on $4600\times$ less data. Qualitative analysis of the resulting embedding spaces further reveals morphologically meaningful cluster structure, with improved separation of kinematically similar activity classes.

Figures & tables

Figure 1: Morphological Contrastive Learning (MorphCL) explicitly models global motion structure by (i) discovering recurring motifs (red) from raw accelerometer data, (ii) clustering them into morphological groups across the pretraining corpus, (iii) defining a group-based contrastive objective that drives agreement beyond instance-level comparisons.

Dataset	Scenario	Activity Types	#Sbjs	#Cls	Avg. Recording (min)
Bock et al. (2024)	outdoor sports workouts	periodic, complex	22	18(+1)	52.52 ( $\pm$ 17.89)
Scholl et al. (2015)	wetlab DNA extraction	(non-)periodic	22	8(+1)	47.93 ( $\pm$ 7.48)
Sztyler & Stuckenschmidt (2016)	locomotion	periodic	13	8	75.84 ( $\pm$ 4.55)
Hoelzemann et al. (2023)	basketball practice and game	(non-)periodic	24	5(+1)	60.59 ( $\pm$ 10.84)
Reiss & Stricker (2012)	locomotion and ADL	periodic, complex	9	18(+1)	50.46 ( $\pm$ 17.79)

Table 1: Downstream datasets. #Sbjs: participants; #Cls: activities (+1 indicates dataset provides a NULL-class). Recording duration is averaged across participants. Activities are categorized into periodic (recurring, periodic movements), non-periodic (sporadic, often short movements/gestures) and complex activities (sequential combinations of periodic and non-periodic movements).

	DeepConvLSTM WISDM			ViT-S WISDM			ViT-S CAPTURE-24
Method	Base	+ MorphCL	$\Delta$	Base	+ MorphCL	$\Delta$	Base	+ MorphCL	$\Delta$
MTL	$38.31$ $\pm 0.06$	$\mathbf{48.59}$ $\pm 0.25$	$+10.28$	$47.41$ $\pm 0.22$	$\mathbf{50.53}$ $\pm 0.45$	$+3.13$	$47.29$ $\pm 0.12$	$\mathbf{52.12}$ $\pm 0.33$	$+4.83$
SimCLR	$46.27$ $\pm 0.09$	$\mathbf{47.85}$ $\pm 0.26$	$+1.58$	$48.78$ $\pm 0.57$	$\mathbf{50.82}$ $\pm 0.50$	$+2.04$	$\mathbf{51.36}$ $\pm 0.20$	$50.97$ $\pm 0.23$	$-0.39$
Masked AE	–	–	–	$44.28$ $\pm 0.87$	$\mathbf{50.57}$ $\pm 0.50$	$+6.29$	$47.56$ $\pm 0.38$	$\mathbf{50.05}$ $\pm 0.37$	$+2.49$
Denoising AE	–	–	–	$47.73$ $\pm 0.49$	$\mathbf{49.32}$ $\pm 0.33$	$+1.59$	$48.27$ $\pm 0.23$	$\mathbf{51.13}$ $\pm 0.33$	$+2.86$
REBAR	$37.81$ $\pm 0.19$	$\mathbf{46.43}$ $\pm 0.17$	$+8.62$	$41.48$ $\pm 0.68$	$\mathbf{49.81}$ $\pm 0.39$	$+8.32$	$37.09$ $\pm 0.13$	$\mathbf{48.27}$ $\pm 0.49$	$+11.18$

Table 2: Linear probing macro F1 (%), averaged over the five downstream datasets. Standard deviations across seeded runs appear below the means. Bold : higher mean per method and encoder/pretraining setting. Dataset-level results are provided in Appendix B .

	DeepConvLSTM WISDM			ViT-S WISDM			ViT-S CAPTURE-24
Method	Base	+ MorphCL	$\Delta$	Base	+ MorphCL	$\Delta$	Base	+ MorphCL	$\Delta$
MTL	$54.18$ $\pm 0.44$	$\mathbf{58.07}$ $\pm 0.22$	$+3.89$	$57.51$ $\pm 0.27$	$\mathbf{58.23}$ $\pm 0.16$	$+0.71$	$60.26$ $\pm 0.15$	$\mathbf{60.68}$ $\pm 0.11$	$+0.42$
SimCLR	$57.32$ $\pm 0.31$	$\mathbf{57.57}$ $\pm 0.28$	$+0.24$	$55.28$ $\pm 0.25$	$\mathbf{56.25}$ $\pm 0.19$	$+0.97$	$58.22$ $\pm 0.39$	$\mathbf{58.72}$ $\pm 0.19$	$+0.51$
Masked AE	–	–	–	$54.36$ $\pm 0.32$	$\mathbf{56.18}$ $\pm 0.16$	$+1.81$	$55.61$ $\pm 0.32$	$\mathbf{57.98}$ $\pm 0.13$	$+2.37$
Denoising AE	–	–	–	$53.74$ $\pm 0.24$	$\mathbf{55.27}$ $\pm 0.13$	$+1.53$	$55.31$ $\pm 0.29$	$\mathbf{58.35}$ $\pm 0.25$	$+3.04$
REBAR	$53.23$ $\pm 0.29$	$\mathbf{57.01}$ $\pm 0.04$	$+3.78$	$52.95$ $\pm 0.21$	$\mathbf{54.83}$ $\pm 0.24$	$+1.88$	$54.05$ $\pm 0.13$	$\mathbf{58.12}$ $\pm 0.16$	$+4.07$

Table 3: Finetuning macro F1 (%), averaged over the five downstream datasets (mean

\pm

standard deviation across seeded runs). Bold : higher mean per method and encoder/pretraining setting. Dataset-level results are provided in Appendix B .

	MTL			SimCLR			Masked AE
Subset	Base	+ MorphCL	$\Delta$	Base	+ MorphCL	$\Delta$	Base	+ MorphCL	$\Delta$
Full	$47.41$ $\pm 0.22$	$\mathbf{50.53}$ $\pm 0.45$	$+3.13$	$48.78$ $\pm 0.57$	$\mathbf{50.82}$ $\pm 0.50$	$+2.04$	$44.28$ $\pm 0.87$	$\mathbf{50.57}$ $\pm 0.50$	$+6.29$
Motif	$46.13$ $\pm 0.36$	$\mathbf{49.70}$ $\pm 0.79$	$+3.57$	$47.75$ $\pm 0.62$	$\mathbf{50.53}$ $\pm 0.69$	$+2.78$	$43.56$ $\pm 0.66$	$\mathbf{50.06}$ $\pm 0.47$	$+6.50$
Morph	$46.86$ $\pm 0.36$	$\mathbf{49.42}$ $\pm 0.49$	$+2.56$	$47.88$ $\pm 0.62$	$\mathbf{49.80}$ $\pm 0.50$	$+1.92$	$42.42$ $\pm 0.87$	$\mathbf{50.12}$ $\pm 0.88$	$+7.70$

Table 4: Subset pretraining on WISDM (linear probing macro F1 (%), averaged over five downstream datasets; mean

\pm

standard deviation across seeded runs). + MorphCL adds the auxiliary MorphCL loss computed on the Morph subset. Bold : higher mean per SSL-subset pair.

Figure 2: Sensitivity of MTL + MorphCL on WISDM. Mean linear-probing macro F1 (%) over five downstream datasets. Diamonds mark defaults:

\sigma=1

r=\rho=\mathrm{support}=100

Figure 3: UMAP ( McInnes et al., 2020 ) embeddings of a WISDM participant from ViT-S models pretrained on CAPTURE-24 using (i) MTL, (ii) MorphCL and (iii) MTL + MorphCL. SC: silhouette coefficient ( $[-1,1]$ ; higher is better). On unseen data, MTL yields entangled clusters, while MorphCL captures global structure. Their combination improves cluster compactness and separation (higher SC), distinguishing sitting from typing (yellow/purple circles) while preserving coherent manifolds among vigorous activities (dashed circle).

Hours of Data

Model Size

WEAR

Wetlab

PAMAP2

Hangtime

RWHAR

Avg.

LProbe

Fine

LProbe

Fine

LProbe

Fine

LProbe

Fine

LProbe

Fine

LProbe

Fine

Yuan et al. (2024) - MTL

ResNet18

3.7K

10.4M

33.77

\pm 0.24

77.68

\pm 0.56

18.53

\pm 0.35

41.38

\pm 0.37

54.15

\pm 0.68

75.01

\pm 2.76

24.54

\pm 0.32

29.75

\pm 0.14

50.22

\pm 0.61

79.21

\pm 0.46

36.24

\pm 0.18

60.61

\pm 0.60

ResNet18

16.8M

10.4M

71.45

\pm 0.16

83.86

\pm 0.41

35.00

\pm 0.49

48.13

\pm 0.42

77.06

\pm 0.62

84.00

\pm 1.38

29.80

\pm 0.22

29.68

\pm 0.07

76.70

\pm 0.60

86.30

\pm 0.63

58.00

\pm 0.05

66.39

\pm 0.39

Ours - MTL + MorphCL

ResNet18

3.7K

10.4M

72.40

\pm 0.21

80.61

\pm 0.14

39.98

\pm 0.61

47.02

\pm 0.54

75.15

\pm 1.07

75.80

\pm 1.71

29.64

\pm 0.07

28.73

\pm 0.09

76.03

\pm 1.05

80.74

\pm 1.59

58.64

\pm 0.31

62.58

\pm 0.27

Table 5: Comparison with pretrained models from Yuan et al. (2024) . Macro F1 (%; mean

\pm

standard deviation across seeded runs) is reported for linear probing and finetuning.

Appendix figures & tables19 assets

Supplementary material from the paper’s appendix.

Appendix

Pretraining Corpus	Unfiltered		Motif-filtered		Morph-filtered
Pretraining Corpus	Train	Test	Train	Test	Train	Test
WISDM	35.5	9.7	15.2	4.4	3.2	0.8
CAPTURE-24	3004.6	787.4	783.5	197.7	47.3	39.0

Appendix

Table 6: Size comparison of the different pretraining corpora. We compare the unfiltered version of the WISDM and CAPTURE-24 dataset, created by evenly splitting the dataset in windows of

2.56s

without overlap, with our proposed motif-filtered variant as described in Chapter 3.1 and the morphological-clustered variant as described in Chapter 3.2 . Values are provided as hours of data.

WEAR

Wetlab

PAMAP2

Hangtime

RWHAR

Avg.

LProbe

Fine

LProbe

Fine

LProbe

Fine

LProbe

Fine

LProbe

Fine

LProbe

Fine

Encoder: DeepConvLSTM

Fully-supervised

\mathit{61.42}

\pm 0.63

\mathit{29.34}

\pm 0.38

\mathit{65.46}

\pm 0.93

\mathit{52.04}

\pm 0.31

\mathit{63.89}

\pm 0.70

\mathit{54.43}

\pm 0.17

Pretraining: WISDM

MTL

37.72

\pm 0.34

57.86

\pm 0.53

17.63

\pm 0.07

27.99

\pm 0.19

54.84

\pm 0.18

69.69

\pm 0.16

27.03

\pm 0.56

48.59

\pm 0.23

54.31

\pm 0.44

66.77

\pm 1.46

38.31

\pm 0.06

54.18

\pm 0.44

SimCLR

46.74

\pm 0.11

60.66

\pm 0.25

21.21

\pm 0.41

32.58

\pm 0.25

61.47

\pm 0.22

73.64

\pm 0.80

38.64

\pm 0.36

52.40

\pm 0.12

63.29

\pm 0.45

67.35

\pm 0.89

46.27

\pm 0.09

57.32

\pm 0.31

Appendix

Table 7: Downstream result comparison of the proposed morphological learning with instance-based self-supervised learning methods. Results are reported for five downstream benchmark datasets. Pretraining was performed on both the WISDM and CAPTURE-24 dataset using different encoder backbones. Fully-supervised learning refers to training the backbone without prior pretraining directly on the downstream dataset. Macro F1 is reported in % as mean

\pm

standard deviation across seeded runs. Shaded rows denote standalone MorphCL.

	WEAR	Wetlab	PAMAP2	Hangtime	RWHAR	Avg.
Encoder: DeepConvLSTM; Pretraining: WISDM
MTL	$37.72$ $\pm 0.34$	$17.63$ $\pm 0.07$	$54.84$ $\pm 0.18$	$27.03$ $\pm 0.56$	$54.31$ $\pm 0.44$	$38.31$ $\pm 0.06$
MTL + MorphCL	$50.58$ $\pm 0.34$ ( $+12.86$ )	$23.72$ $\pm 0.33$ ( $+6.09$ )	$66.99$ $\pm 0.54$ ( $+12.16$ )	$36.03$ $\pm 0.59$ ( $+9.00$ )	$65.61$ $\pm 1.12$ ( $+11.30$ )	$48.59$ $\pm 0.25$ ( $+10.28$ )
SimCLR	$46.74$ $\pm 0.11$	$21.21$ $\pm 0.41$	$61.47$ $\pm 0.22$	$38.64$ $\pm 0.36$	$63.29$ $\pm 0.45$	$46.27$ $\pm 0.09$
SimCLR + MorphCL	$48.41$ $\pm 0.49$ ( $+1.67$ )	$23.43$ $\pm 0.42$ ( $+2.22$ )	$63.57$ $\pm 0.61$ ( $+2.10$ )	$39.52$ $\pm 0.43$ ( $+0.88$ )	$64.33$ $\pm 0.31$ ( $+1.04$ )	$47.85$ $\pm 0.26$ ( $+1.58$ )
REBAR	$33.64$ $\pm 0.26$	$18.16$ $\pm 0.05$	$53.86$ $\pm 0.28$	$24.25$ $\pm 0.22$	$59.15$ $\pm 0.77$	$37.81$ $\pm 0.19$

Appendix

Table 8: Absolute percentage point improvements on downstream linear probing results of instance-based self-supervised learning methods if combined with an additional morphological loss term. Results are reported for five downstream benchmark datasets. Pretraining was performed on both the WISDM and CAPTURE-24 dataset using different encoder backbones. Macro F1 is reported in % as mean

\pm

standard deviation across seeded runs.

	WEAR	Wetlab	PAMAP2	Hangtime	RWHAR	Avg.
Encoder: DeepConvLSTM; Pretraining: WISDM
MTL	$57.86$ $\pm 0.53$	$27.99$ $\pm 0.19$	$69.69$ $\pm 0.16$	$48.59$ $\pm 0.23$	$66.77$ $\pm 1.46$	$54.18$ $\pm 0.44$
MTL + MorphCL	$62.59$ $\pm 0.46$ ( $+4.74$ )	$33.36$ $\pm 0.17$ ( $+5.37$ )	$72.37$ $\pm 1.04$ ( $+2.67$ )	$52.99$ $\pm 0.54$ ( $+4.40$ )	$69.06$ $\pm 0.92$ ( $+2.29$ )	$58.07$ $\pm 0.22$ ( $+3.89$ )
SimCLR	$60.66$ $\pm 0.25$	$32.58$ $\pm 0.25$	$73.64$ $\pm 0.80$	$52.40$ $\pm 0.12$	$67.35$ $\pm 0.89$	$57.32$ $\pm 0.31$
SimCLR + MorphCL	$62.33$ $\pm 0.28$ ( $+1.67$ )	$33.46$ $\pm 0.30$ ( $+0.88$ )	$71.68$ $\pm 0.97$ ( $-1.96$ )	$53.15$ $\pm 0.22$ ( $+0.75$ )	$67.21$ $\pm 1.41$ ( $-0.14$ )	$57.57$ $\pm 0.28$ ( $+0.24$ )
REBAR	$56.64$ $\pm 0.20$	$27.76$ $\pm 0.08$	$67.06$ $\pm 0.64$	$50.89$ $\pm 0.39$	$63.81$ $\pm 0.75$	$53.23$ $\pm 0.29$

Appendix

Table 9: Absolute finetuning percentage point improvement of existing SSL methods if combined with MorphCL. Results are seed-averaged across 3 runs. All frameworks benefit from being combined with MorphCL with the MTL framework being the best performing method. Macro F1 is reported in % as mean

\pm

standard deviation across seeded runs.

$\sigma$	Discovered motifs	Extracted windows	Relative to full dataset	Downstream F1
$0.5$	$636$	$15,816$	$31.69\%$	$49.61$ $\pm 0.64$
$1.0$	$758$	$21,318$	$42.71\%$	$50.53$ $\pm 0.45$
$1.5$	$788$	$23,202$	$46.49\%$	$49.89$ $\pm 0.61$
$2.0$	$690$	$20,793$	$41.66\%$	$49.67$ $\pm 0.58$

Appendix

Table 10: Hyperparameter sweep for

\sigma

with fixed

r=100

for a motif length of 0.2s.

$r$	Discovered motifs	Extracted windows	Relative to full dataset	Downstream F1
$50$	$1,145$	$19,943$	$40.00\%$	$49.95$ $\pm 0.75$
$100$	$758$	$21,318$	$42.71\%$	$50.53$ $\pm 0.45$
$150$	$656$	$22,899$	$45.88\%$	$49.88$ $\pm 0.75$
$200$	$544$	$22,956$	$45.99\%$	$49.81$ $\pm 0.55$

Appendix

Table 11: Hyperparameter sweep for

r

with fixed

\sigma=1.0

for a motif length of 0.2s.

$\rho$	$n_{\text{clusters}}$	Noise	DBCV	AMI (vs. activity)	Downstream F1
$50$	$19$	$74.70\%$	$0.3220$	$0.6954$	$50.74$ $\pm 0.60$
$100$	$8$	$78.83\%$	$0.2434$	$0.7122$	$50.53$ $\pm 0.45$
$200$	$6$	$82.38\%$	$0.3287$	$0.6595$	$50.01$ $\pm 0.74$
$400$	–	$100\%$	–	–	–

Appendix

Table 12: Hyperparameter sweep for

\rho

with fixed support

=100

Support	$n_{\text{clusters}}$	Noise	DBCV	AMI (vs. activity)	Downstream F1
$50$	$8$	$78.83\%$	$0.2434$	$0.7122$	$50.73$ $\pm 0.60$
$100$	$8$	$78.83\%$	$0.2434$	$0.7122$	$50.53$ $\pm 0.45$
$200$	$6$	$80.16\%$	$0.2245$	$0.6674$	$49.67$ $\pm 0.80$
$400$	$2$	$14.64\%$	$0.0622$	$0.0357$	$49.28$ $\pm 0.47$

Appendix

Table 13: Hyperparameter sweep support with fixed

\rho=100

Method	Full	Motif	Morph	Random ( $N$ =Motif)	Random ( $N$ =Morph)
SimCLR	$48.78$ $\pm 0.57$	$47.75$ $\pm 0.62$	$47.88$ $\pm 0.62$	$46.69$ $\pm 0.56$	$46.29$ $\pm 0.43$
SimCLR + MorphCL	$\mathbf{50.82}$ $\pm 0.50$	$\mathbf{50.53}$ $\pm 0.69$	$\mathbf{49.80}$ $\pm 0.50$	$\mathbf{49.51}$ $\pm 0.36$	$\mathbf{49.16}$ $\pm 0.58$
MTL	$47.41$ $\pm 0.22$	$46.13$ $\pm 0.36$	$46.86$ $\pm 0.36$	$45.80$ $\pm 0.42$	$45.96$ $\pm 0.41$
MTL + MorphCL	$\mathbf{50.53}$ $\pm 0.45$	$\mathbf{49.70}$ $\pm 0.79$	$\mathbf{49.42}$ $\pm 0.49$	$\mathbf{49.46}$ $\pm 0.59$	$\mathbf{48.50}$ $\pm 0.41$
Denoising AE	$47.73$ $\pm 0.49$	$47.11$ $\pm 0.71$	$48.68$ $\pm 0.48$	$46.64$ $\pm 0.66$	$45.94$ $\pm 0.64$
Denoising AE + MorphCL	$\mathbf{49.32}$ $\pm 0.33$	$\mathbf{49.75}$ $\pm 0.59$	$\mathbf{50.25}$ $\pm 0.74$	$\mathbf{49.47}$ $\pm 0.86$	$\mathbf{49.68}$ $\pm 0.71$

Appendix

Table 14: Downstream F1 scores (%; mean

\pm

standard deviation) for different subset pretraining on the WISDM dataset.

Method	Full	Motif	Morph	Random ( $N$ =Motif)	Random ( $N$ =Morph)
SimCLR	$\mathbf{51.36}$ $\pm 0.20$	$52.05$ $\pm 0.47$	$45.93$ $\pm 0.44$	$50.14$ $\pm 0.47$	$46.51$ $\pm 0.46$
SimCLR + MorphCL	$50.97$ $\pm 0.23$	$\mathbf{52.16}$ $\pm 0.48$	$\mathbf{48.12}$ $\pm 0.80$	$\mathbf{51.34}$ $\pm 0.45$	$\mathbf{49.26}$ $\pm 0.43$
MTL	$47.29$ $\pm 0.12$	$49.73$ $\pm 0.85$	$49.48$ $\pm 0.59$	$45.08$ $\pm 0.47$	$46.61$ $\pm 0.53$
MTL + MorphCL	$\mathbf{52.12}$ $\pm 0.33$	$\mathbf{52.62}$ $\pm 0.52$	$\mathbf{51.41}$ $\pm 0.46$	$\mathbf{50.92}$ $\pm 0.41$	$\mathbf{50.61}$ $\pm 0.60$
Denoising AE	$48.27$ $\pm 0.23$	$46.65$ $\pm 0.42$	$46.21$ $\pm 0.58$	$46.62$ $\pm 0.57$	$44.77$ $\pm 0.68$
Denoising AE + MorphCL	$\mathbf{51.13}$ $\pm 0.33$	$\mathbf{50.26}$ $\pm 0.61$	$\mathbf{49.11}$ $\pm 0.51$	$\mathbf{49.90}$ $\pm 0.67$	$\mathbf{49.57}$ $\pm 0.41$

Appendix

Table 15: Downstream F1 scores (%; mean

\pm

standard deviation) for different subset pretraining on the CAPTURE-24 dataset.

Method	Balanced + Oversampling	Balanced + Undersampling	Unbalanced
SimCLR + MorphCL	$50.82$ $\pm 0.50$	$50.67$ $\pm 0.28$	$50.57$ $\pm 0.60$
MTL + MorphCL	$50.53$ $\pm 0.45$	$50.42$ $\pm 0.51$	$50.28$ $\pm 0.48$

Appendix

Table 16: Downstream F1 scores for different balancing strategies for the MorphCL loss pretraining on the WISDM dataset.

Method	Balanced + Oversampling	Balanced + Undersampling	Unbalanced
SimCLR + MorphCL	$50.97$ $\pm 0.23$	$51.59$ $\pm 0.65$	$52.08$ $\pm 0.57$
MTL + MorphCL	$52.12$ $\pm 0.33$	$52.31$ $\pm 0.57$	$52.24$ $\pm 0.60$

Appendix

Table 17: Downstream F1 scores for different balancing strategies for the MorphCL loss pretraining on the CAPTURE-24 dataset.

$\lambda=0$	$\lambda=1$	$\lambda=5$	$\lambda=10$	$\lambda=20$
$47.41$ $\pm 0.22$	$50.53$ $\pm 0.45$	$49.96$ $\pm 0.99$	$49.33$ $\pm 0.66$	$48.98$ $\pm 0.84$

Appendix

Table 18: Ablation on different loss scaling factors for MorphCL loss term on WISDM dataset.

Framework	WEAR	Wetlab	PAMAP2	Hangtime	RWHAR	WISDM	Avg
Encoder: DeepConvLSTM
Pretraining: WISDM
MTL	-0.1776	-0.2189	0.0533	-0.0471	-0.0639	-0.2035	-0.1096
MTL + MorphCL	-0.2019	-0.2025	0.1088	-0.0493	-0.0872	-0.1682	-0.1001
SimCLR	-0.1990	-0.2002	0.0449	-0.0742	-0.0928	-0.1814	-0.1171
SimCLR + MorphCL	-0.1906	-0.1964	0.0741	-0.0780	-0.1096	-0.1961	-0.1161

Appendix

Table 19: Silhouette coefficients (SC

\in

[-1, 1], higher is better) based on activity classes computed on embeddings extracted from pretrained models using each tested SSL framwork (with and without MorphCL). We report scores based on all participants in each downstream dataset using cosine similarity and excluding background classes if present. Adding MorphCL increases the mean silhouette coefficient across datasets for each evaluated configuration.

Figure 4: UMAP features and Silhouette coefficients (SC) extracted from a ViT-S pretrained on CAPTURE-24 applied to the RWHAR dataset using (i) MTL, (ii) MorphCL and (iii) MTL + MorphCL as pretext task. One can see a clear improvement when combining MorphCL with MTL with even kinematically close classes such as walking stairs (”climbingup”/”climbingdown”) and walking are better separated.

Figure 5: Linear probing results of a ViT-S pretrained on CAPTURE-24 applied to the RWHAR dataset using (i) MTL, (ii) MorphCL and (iii) MTL + MorphCL as pretext task. When combining MorphCL with MTL overall confusion across both kinematically close (e.g., ”jumping” and ”running”) is decreased.

Figure 6: UMAP features and Silhouette coefficients (SC) extracted from a ViT-S pretrained on CAPTURE-24 applied to the Hang-Time dataset using (i) MTL, (ii) MorphCL and (iii) MTL + MorphCL as pretext task. Even with the presence of a dominant background class (”null”), combining MorphCL with MTL still lead to overall better separation of all classes.

Figure 7: Linear probing results of a ViT-S pretrained on CAPTURE-24 applied to the Hang-Time dataset using (i) MTL, (ii) MorphCL and (iii) MTL + MorphCL as pretext task. One can see a clear improvement when combining MorphCL with MTL with even sporadic classes like passes being recognizable.

	WEAR	Wetlab	PAMAP2	Hangtime	RWHAR
Encoder: DeepConvLSTM; Pretraining: WISDM
Khosla et al. (2020)	$44.98$ $\pm 0.34$	$23.89$ $\pm 0.27$	$63.40$ $\pm 0.43$	$34.80$ $\pm 0.42$	$64.68$ $\pm 0.71$
Pillai et al. (2025)	$44.94$ $\pm 0.30$	$23.96$ $\pm 0.23$	$63.34$ $\pm 0.44$	$34.59$ $\pm 0.41$	$64.25$ $\pm 0.48$
Ours	$46.50$ $\pm 0.30$	$23.94$ $\pm 0.24$	$64.39$ $\pm 0.50$	$35.06$ $\pm 0.40$	$64.29$ $\pm 0.55$
Encoder: ViT-S; Pretraining: WISDM
Khosla et al. (2020)	$53.20$ $\pm 0.52$	$25.75$ $\pm 0.53$	$66.77$ $\pm 0.89$	$35.71$ $\pm 0.86$	$68.18$ $\pm 0.87$

Appendix

Table 20: Ablation study on using losses as reported by Khosla et al. (2020) and Pillai et al. (2025) . Results are averaged across the MorphCL framework itself as well as its combination with the different SSL frameworks reported in the main paper. Best results are in bold .

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Inertia-1: An Open Exploration of Wearable Motion Foundation Models

Zongzhe Xu, Aakarsh Anand, Sarah Jiang, Chuntung Zhuang+3

University of California, Los Angeles · Duke University · John Hopkins University

Human Activity Recognition Wearable Sensing Motion Representation Learning Time-Series Foundation Models

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Wearable motion sensing provides a continuous and scalable window into human behavior and health, making it a natural fit for foundation models, yet its pretraining and scaling principles remain poorly understood. Prior work studies isolated design choices, such as sensor placement or sampling frequency, often under fixed settings and narrow downstream tasks that fail to capture real-world sensing diversity. We introduce Inertia-1, a fully open exploration of wearable motion foundation models. Using massive corpora of accelerometer data from global sources spanning more than 18.2M hours, we build a controlled framework for studying the full lifecycle of wearable motion foundation models, covering data choices such as sensor modality, device placement, sampling rate, window length; model choices such as architectures and model size; and training choices such as pretraining objective and data scale. Extensive evaluations across 15 datasets spanning human activity recognition, freezing-of-gait detection, and disease prediction reveal intriguing findings for building motion foundation models that generalize across tasks and sensing conditions. Collectively, Inertia-1 not only presents state-of-the-art recipes for diverse downstream tasks, but also serves as a comprehensive, practical, and open cookbook for wearable motion representation learning.

Abstract

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