WHAR Arena | Leaderboard

Model
CNN-HAR A*STAR	2.61% ±3.64%	CNN ⓘ	67.7%	0.711 0.93 MB	0.735 4.12 MB	0.592 11.3 ms	0.673
TinyHAR Karlsruhe Institute of Technology	2.50% ±3.23%	Attention ⓘCNN ⓘDense ⓘRNN ⓘ	67.6%	0.698 1.0 MB	0.641 6.65 MB	0.614 9.78 ms	0.649
Triple-Cross-Attn Nanjing Normal University · Southeast University	2.47% ±3.36%	Attention ⓘCNN ⓘDense ⓘ	67.6%	0.552 3.1 MB	0.601 8.17 MB	0.576 12.6 ms	0.576
DANA Imperial College London · Queen Mary University of London	1.92% ±3.62%	CNN ⓘDense ⓘRNN ⓘ	67.0%	0.495	0.552 10.1 MB	0.467 25.2 ms	0.503
TinierHAR German Research Center for Artificial Intelligence (DFKI)	1.62% ±3.29%	Attention ⓘCNN ⓘDense ⓘRNN ⓘ	66.7%	0.967 0.11 MB	0.967 1.05 MB	0.729 4.45 ms	0.841
Attend+Discriminate The University of Adelaide	1.51% ±3.50%	Attention ⓘCNN ⓘDense ⓘRNN ⓘ	66.6%	0.528 3.6 MB	0.292 39.9 MB	0.301 79.7 ms	0.364
DynamicWHAR Zhejiang University	1.16% ±2.72%	CNN ⓘDense ⓘGraph ⓘ	66.3%	0.351 13.6 MB	0.492 14.1 MB	0.429 33.5 ms	0.421
Random Forest Baseline	0.00% ±0.00%	Features ⓘ	65.1%	0.606 1.9 MB	0.470 15.5 MB	0.914 0.73 ms	0.616
DeepConvLSTM-Attn Georgia Institute of Technology	-0.43% ±3.43%	Attention ⓘCNN ⓘDense ⓘRNN ⓘ	64.7%	0.487 4.6 MB	0.619 6.96 MB	0.316 69.0 ms	0.460
MLP-HAR Karlsruhe Institute of Technology	-0.87% ±2.77%	Dense ⓘSpectral ⓘ	64.2%	0.449 6.1 MB	0.617 6.87 MB	0.633 7.57 ms	0.559
DeepConvLSTM University of Sussex	-1.49% ±3.37%	CNN ⓘDense ⓘRNN ⓘ	63.6%	0.481 4.5 MB	0.621 6.50 MB	0.308 70.2 ms	0.455
MLP-Mixer University of Southampton	-2.65% ±4.55%	Dense ⓘ	62.5%	0.509 3.3 MB	0.653 4.92 MB	0.484 19.1 ms	0.543
Guan-LSTM Newcastle University · Georgia Institute of Technology	-5.04% ±4.22%	Dense ⓘRNN ⓘ	60.1%	0.478 3.3 MB	0.652 3.62 MB	0.290 63.4 ms	0.453
DeepConvShallowLSTM University of Siegen	-5.15% ±3.09%	CNN ⓘDense ⓘRNN ⓘ	60.0%	0.444 4.3 MB	0.543 7.37 MB	0.379 32.7 ms	0.451
SA-HAR University of Dhaka · Independent University Bangladesh	-5.26% ±3.28%	Attention ⓘCNN ⓘDense ⓘ	59.8%	0.542 1.8 MB	0.593 5.29 MB	0.497 13.2 ms	0.542
SVM Baseline	-14.95% ±2.97%	Features ⓘ	50.2%	0.150 11.3 MB	0.138 28.2 MB	0.387 2.84 ms	0.216
k-NN Baseline	-18.85% ±3.34%	Features ⓘ	46.3%	0.000 21.2 MB	0.081 21.6 MB	0.000 84.7 ms	0.026

Model

CNN-HAR A*STAR

2.61% ±3.64%

CNN ⓘ

67.7%

0.711 0.93 MB

0.735 4.12 MB

0.592 11.3 ms

0.673

TinyHAR Karlsruhe Institute of Technology

2.50% ±3.23%

Attention ⓘCNN ⓘDense ⓘRNN ⓘ

67.6%

0.698 1.0 MB

0.641 6.65 MB

0.614 9.78 ms

0.649

Triple-Cross-Attn Nanjing Normal University · Southeast University

2.47% ±3.36%

Attention ⓘCNN ⓘDense ⓘ

67.6%

0.552 3.1 MB

0.601 8.17 MB

0.576 12.6 ms

0.576

DANA Imperial College London · Queen Mary University of London

1.92% ±3.62%

CNN ⓘDense ⓘRNN ⓘ

67.0%

0.495

0.552 10.1 MB

0.467 25.2 ms

0.503

TinierHAR German Research Center for Artificial Intelligence (DFKI)

1.62% ±3.29%

Attention ⓘCNN ⓘDense ⓘRNN ⓘ

66.7%

0.967 0.11 MB

0.967 1.05 MB

0.729 4.45 ms

0.841

Attend+Discriminate The University of Adelaide

1.51% ±3.50%

Attention ⓘCNN ⓘDense ⓘRNN ⓘ

66.6%

0.528 3.6 MB

0.292 39.9 MB

0.301 79.7 ms

0.364

DynamicWHAR Zhejiang University

1.16% ±2.72%

CNN ⓘDense ⓘGraph ⓘ

66.3%

0.351 13.6 MB

0.492 14.1 MB

0.429 33.5 ms

0.421

Random Forest Baseline

0.00% ±0.00%

Features ⓘ

65.1%

0.606 1.9 MB

0.470 15.5 MB

0.914 0.73 ms

0.616

DeepConvLSTM-Attn Georgia Institute of Technology

-0.43% ±3.43%

Attention ⓘCNN ⓘDense ⓘRNN ⓘ

64.7%

0.487 4.6 MB

0.619 6.96 MB

0.316 69.0 ms

0.460

MLP-HAR Karlsruhe Institute of Technology

-0.87% ±2.77%

Dense ⓘSpectral ⓘ

64.2%

0.449 6.1 MB

0.617 6.87 MB

0.633 7.57 ms

0.559

DeepConvLSTM University of Sussex

-1.49% ±3.37%

CNN ⓘDense ⓘRNN ⓘ

63.6%

0.481 4.5 MB

0.621 6.50 MB

0.308 70.2 ms

0.455

MLP-Mixer University of Southampton

-2.65% ±4.55%

Dense ⓘ

62.5%

0.509 3.3 MB

0.653 4.92 MB

0.484 19.1 ms

0.543

Guan-LSTM Newcastle University · Georgia Institute of Technology

-5.04% ±4.22%

Dense ⓘRNN ⓘ

60.1%

0.478 3.3 MB

0.652 3.62 MB

0.290 63.4 ms

0.453

DeepConvShallowLSTM University of Siegen

-5.15% ±3.09%

CNN ⓘDense ⓘRNN ⓘ

60.0%

0.444 4.3 MB

0.543 7.37 MB

0.379 32.7 ms

0.451

SA-HAR University of Dhaka · Independent University Bangladesh

-5.26% ±3.28%

Attention ⓘCNN ⓘDense ⓘ

59.8%

0.542 1.8 MB

0.593 5.29 MB

0.497 13.2 ms

0.542

SVM Baseline

-14.95% ±2.97%

Features ⓘ

50.2%

0.150 11.3 MB

0.138 28.2 MB

0.387 2.84 ms

0.216

k-NN Baseline

-18.85% ±3.34%

Features ⓘ

46.3%

0.000 21.2 MB

0.081 21.6 MB

0.000 84.7 ms

0.026


95.6	97.7	90.8	93.4	91.2	95.7	95.7	98.3	91.4	97.4	91.1	93.5	90.1	95.5	81.2	90.4	82.1
96.0	96.1	96.0	95.0	95.2	94.0	92.1	84.7	92.6	93.2	93.0	94.6	91.5	90.0	92.3	72.7	71.2
91.5	91.2	91.3	88.9	90.9	88.6	93.5	91.5	88.5	91.2	87.6	91.2	90.5	88.6	85.3	76.5	67.4
86.9	86.5	88.5	87.0	87.0	85.6	89.6	87.3	84.2	85.3	83.3	84.0	82.8	81.1	84.2	75.9	69.7
86.9	86.8	85.6	84.4	86.4	86.2	87.8	89.3	83.8	84.3	82.0	81.5	83.2	85.7	77.4	75.9	70.9
86.1	80.5	81.3	83.0	84.8	84.0	85.7	87.6	80.5	80.8	78.0	77.2	78.4	81.9	76.5	76.6	64.2
81.4	85.0	83.5	85.5	83.8	83.8	81.9	71.6	84.7	76.1	84.0	83.0	84.2	83.0	65.1	59.2	51.2
87.0	88.3	87.2	84.1	86.3	86.4	69.0	84.9	80.1	85.4	79.3	66.1	68.3	80.2	80.7	52.8	38.3
84.8	81.0	82.0	83.6	81.4	81.4	79.4	67.1	79.5	79.2	77.3	80.5	73.4	59.7	75.7	63.9	66.3
82.5	80.7	78.1	80.0	78.6	80.0	78.8	75.4	75.4	73.0	77.7	80.3	75.0	77.9	79.2	65.2	47.6
76.2	78.1	74.3	71.1	75.7	73.3	75.7	72.7	71.0	76.5	73.5	78.8	68.9	62.9	69.8	37.7	43.3
68.8	68.7	68.5	71.3	68.6	69.4	68.9	59.8	69.0	61.8	67.2	69.8	70.6	67.8	65.4	49.8	43.9
63.1	65.4	70.6	58.7	65.9	61.4	62.6	70.6	65.2	74.7	62.2	60.3	65.9	65.1	58.1	66.7	59.4
72.4	71.1	72.7	69.9	72.6	66.7	67.5	55.9	63.4	65.1	64.0	66.8	66.7	54.1	61.5	44.3	43.1
64.4	66.3	68.0	66.8	64.9	65.2	70.1	65.6	64.2	64.4	63.3	59.9	60.1	59.7	59.9	51.4	44.5
57.6	60.2	61.7	64.0	60.9	57.2	68.4	80.3	59.3	58.9	55.0	51.8	50.8	67.3	53.0	56.7	47.3
69.2	65.8	59.5	60.6	68.5	62.4	57.5	57.5	59.4	59.8	60.4	62.4	56.2	53.3	56.6	48.6	44.1
62.1	62.2	61.9	60.3	59.6	62.3	64.2	65.1	57.6	63.1	55.8	47.8	51.8	55.9	59.0	48.0	49.0
55.0	61.0	58.7	52.6	55.6	54.5	61.1	72.8	52.2	56.5	52.9	43.2	45.7	54.5	51.7	46.9	40.4
68.2	65.8	67.0	64.8	61.0	67.2	64.7	51.2	59.4	61.6	54.9	64.9	24.7	19.0	61.0	23.9	33.9
58.7	58.0	58.1	60.1	56.0	59.9	57.4	47.6	56.8	53.0	56.3	57.9	59.5	42.9	50.3	36.7	31.7
63.3	58.7	55.2	62.4	58.4	61.0	52.5	44.5	61.1	42.3	56.3	57.2	55.5	38.0	38.7	19.2	23.4
47.6	49.0	49.3	52.6	48.2	53.0	55.0	62.6	48.7	46.3	48.1	42.3	43.3	53.6	42.9	48.7	38.0
51.5	51.1	51.1	52.1	50.9	50.9	47.0	46.9	51.5	47.8	51.5	50.8	52.3	46.5	42.9	36.8	39.2
52.7	52.2	49.8	49.3	49.1	48.6	49.9	53.1	48.8	46.3	47.2	48.9	39.1	35.8	51.0	31.5	32.4
50.0	50.2	52.4	51.8	51.2	50.6	56.2	54.2	45.7	49.1	45.5	33.1	37.3	40.8	41.9	29.8	29.2
49.1	46.6	47.8	44.8	48.3	42.9	44.1	52.5	42.9	46.1	44.3	41.9	44.5	43.9	41.4	41.2	38.5
52.3	51.1	61.5	57.2	45.2	53.8	41.5	41.9	52.5	43.8	48.2	37.8	35.4	46.9	31.1	27.6	28.6
40.2	43.9	42.7	43.6	45.4	43.0	37.8	33.8	41.8	40.6	40.7	38.9	29.2	35.4	37.7	31.7	31.1
30.4	29.2	32.0	31.7	30.3	29.5	32.2	26.8	29.1	23.6	27.6	27.3	26.9	31.7	23.8	18.5	17.6

Dataset

95.6

97.7

90.8

93.4

91.2

95.7

98.3

91.4

97.4

91.1

93.5

90.1

95.5

81.2

90.4

82.1

96.0

96.1

96.0

95.0

95.2

94.0

92.1

84.7

92.6

93.2

93.0

94.6

91.5

90.0

92.3

72.7

71.2

91.5

91.2

91.3

88.9

90.9

88.6

93.5

91.5

88.5

91.2

87.6

91.2

90.5

88.6

85.3

76.5

67.4

86.9

86.5

88.5

87.0

85.6

89.6

87.3

84.2

85.3

83.3

84.0

82.8

81.1

84.2

75.9

69.7

86.9

86.8

85.6

84.4

86.4

86.2

87.8

89.3

83.8

84.3

82.0

81.5

83.2

85.7

77.4

75.9

70.9

86.1

80.5

81.3

83.0

84.8

84.0

85.7

87.6

80.5

80.8

78.0

77.2

78.4

81.9

76.5

76.6

64.2

81.4

85.0

83.5

85.5

83.8

81.9

71.6

84.7

76.1

84.0

83.0

84.2

83.0

65.1

59.2

51.2

87.0

88.3

87.2

84.1

86.3

86.4

69.0

84.9

80.1

85.4

79.3

66.1

68.3

80.2

80.7

52.8

38.3

84.8

81.0

82.0

83.6

81.4

79.4

67.1

79.5

79.2

77.3

80.5

73.4

59.7

75.7

63.9

66.3

82.5

80.7

78.1

80.0

78.6

80.0

78.8

75.4

73.0

77.7

80.3

75.0

77.9

79.2

65.2

47.6

76.2

78.1

74.3

71.1

75.7

73.3

75.7

72.7

71.0

76.5

73.5

78.8

68.9

62.9

69.8

37.7

43.3

68.8

68.7

68.5

71.3

68.6

69.4

68.9

59.8

69.0

61.8

67.2

69.8

70.6

67.8

65.4

49.8

43.9

63.1

65.4

70.6

58.7

65.9

61.4

62.6

70.6

65.2

74.7

62.2

60.3

65.9

65.1

58.1

66.7

59.4

72.4

71.1

72.7

69.9

72.6

66.7

67.5

55.9

63.4

65.1

64.0

66.8

66.7

54.1

61.5

44.3

43.1

64.4

66.3

68.0

66.8

64.9

65.2

70.1

65.6

64.2

64.4

63.3

59.9

60.1

59.7

59.9

51.4

44.5

57.6

60.2

61.7

64.0

60.9

57.2

68.4

80.3

59.3

58.9

55.0

51.8

50.8

67.3

53.0

56.7

47.3

69.2

65.8

59.5

60.6

68.5

62.4

57.5

59.4

59.8

60.4

62.4

56.2

53.3

56.6

48.6

44.1

62.1

62.2

61.9

60.3

59.6

62.3

64.2

65.1

57.6

63.1

55.8

47.8

51.8

55.9

59.0

48.0

49.0

55.0

61.0

58.7

52.6

55.6

54.5

61.1

72.8

52.2

56.5

52.9

43.2

45.7

54.5

51.7

46.9

40.4

68.2

65.8

67.0

64.8

61.0

67.2

64.7

51.2

59.4

61.6

54.9

64.9

24.7

19.0

61.0

23.9

33.9

58.7

58.0

58.1

60.1

56.0

59.9

57.4

47.6

56.8

53.0

56.3

57.9

59.5

42.9

50.3

36.7

31.7

63.3

58.7

55.2

62.4

58.4

61.0

52.5

44.5

61.1

42.3

56.3

57.2

55.5

38.0

38.7

19.2

23.4

47.6

49.0

49.3

52.6

48.2

53.0

55.0

62.6

48.7

46.3

48.1

42.3

43.3

53.6

42.9

48.7

38.0

51.5

51.1

52.1

50.9

47.0

46.9

51.5

47.8

51.5

50.8

52.3

46.5

42.9

36.8

39.2

52.7

52.2

49.8

49.3

49.1

48.6

49.9

53.1

48.8

46.3

47.2

48.9

39.1

35.8

51.0

31.5

32.4

50.0

50.2

52.4

51.8

51.2

50.6

56.2

54.2

45.7

49.1

45.5

33.1

37.3

40.8

41.9

29.8

29.2

49.1

46.6

47.8

44.8

48.3

42.9

44.1

52.5

42.9

46.1

44.3

41.9

44.5

43.9

41.4

41.2

38.5

52.3

51.1

61.5

57.2

45.2

53.8

41.5

41.9

52.5

43.8

48.2

37.8

35.4

46.9

31.1

27.6

28.6

40.2

43.9

42.7

43.6

45.4

43.0

37.8

33.8

41.8

40.6

40.7

38.9

29.2

35.4

37.7

31.7

31.1

30.4

29.2

32.0

31.7

30.3

29.5

32.2

26.8

29.1

23.6

27.6

27.3

26.9

31.7

23.8

18.5

17.6

Mean Test Macro-F1 vs. Latency

Mean Test Macro-F1 vs. Peak PSS Delta

Mean Test Macro-F1 vs. Exported Model Size

@misc{burzer2026whararenabenchmarkingstate, title = {{WHAR Arena}: Benchmarking the State of the Art in Efficient Wearable Human Activity Recognition}, author = {Maximilian Burzer and Tobias King and Till Riedel and Michael Beigl and Tobias R{"o}ddiger}, year = {2026}, eprint = {2606.13194}, archivePrefix = {arXiv}, primaryClass = {cs.LG}, url = {https://arxiv.org/abs/2606.13194} }

Leaderboard

Filters

Leaderboard

Dataset × Model Matrix

Deployment Trade-Offs

Mean Test Macro-F1 vs. Latency

Mean Test Macro-F1 vs. Peak PSS Delta

Mean Test Macro-F1 vs. Exported Model Size

Citation

Acknowledgements