Dataset Viewer
F1
int64 741
805
| F2
int64 811
872
| F3
int64 851
920
| F4
int64 697
891
| F5
int64 771
811
| Acc_Fin_x
int64 -513
209
| Acc_Fin_y
int64 -314
316
| Acc_Fin_z
int64 -375
261
| Acc_Palm_x
int64 -297
128
| Acc_Palm_y
int64 -280
252
| Acc_Palm_z
int64 -204
322
| Acc_Arm_x
int64 -10
1
| Acc_Arm_y
int64 246
264
| Acc_Arm_z
int64 13
43
| label
stringclasses 6
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
750 | 842 | 883 | 773 | 785 | -128 | -201 | -122 | -89 | -246 | 51 | -4 | 250 | 36 | Bad |
749 | 830 | 879 | 712 | 794 | -136 | -208 | -117 | -94 | -238 | 74 | -6 | 249 | 35 | Bad |
759 | 835 | 885 | 850 | 788 | -146 | 51 | 141 | -266 | 14 | 7 | -5 | 249 | 35 | Bad |
750 | 851 | 879 | 875 | 773 | 186 | 114 | 96 | 128 | 198 | 91 | -7 | 251 | 38 | Bad |
757 | 840 | 893 | 705 | 802 | -67 | 167 | 144 | -151 | 212 | -71 | -7 | 250 | 35 | Bad |
743 | 841 | 876 | 858 | 787 | -253 | 69 | -9 | -253 | 0 | -98 | -8 | 251 | 35 | Bad |
745 | 841 | 884 | 885 | 784 | -13 | 38 | 170 | -62 | 192 | 45 | -7 | 251 | 37 | Bad |
743 | 841 | 889 | 867 | 786 | -190 | -94 | 121 | -230 | 3 | 115 | -7 | 250 | 40 | Bad |
752 | 836 | 882 | 844 | 776 | 186 | 45 | 162 | 95 | 204 | 127 | -7 | 250 | 36 | Bad |
754 | 845 | 890 | 891 | 796 | -213 | -23 | 99 | -250 | 36 | 44 | -6 | 248 | 37 | Bad |
756 | 843 | 875 | 870 | 794 | -126 | 189 | 145 | -178 | 221 | -82 | -4 | 250 | 36 | Bad |
742 | 851 | 884 | 770 | 771 | -255 | -210 | -18 | -232 | -188 | 103 | -4 | 250 | 35 | Bad |
750 | 840 | 875 | 798 | 784 | 209 | 46 | 159 | 125 | 221 | 107 | -7 | 250 | 38 | Bad |
744 | 856 | 887 | 846 | 781 | -137 | -202 | -113 | -88 | -242 | 52 | -5 | 249 | 35 | Bad |
749 | 842 | 881 | 799 | 780 | -179 | -44 | 144 | -234 | 54 | 89 | -6 | 249 | 35 | Bad |
746 | 837 | 880 | 887 | 791 | -196 | 4 | 133 | -245 | 79 | 44 | -8 | 252 | 34 | Bad |
757 | 838 | 878 | 842 | 786 | -129 | 240 | 63 | -200 | 216 | -113 | -7 | 251 | 36 | Bad |
746 | 845 | 884 | 844 | 793 | -218 | -1 | 96 | -254 | 38 | 27 | -8 | 252 | 37 | Bad |
752 | 860 | 893 | 884 | 790 | -194 | -37 | 144 | -236 | 95 | 64 | -7 | 249 | 35 | Bad |
741 | 841 | 887 | 706 | 789 | 126 | 12 | 200 | 47 | 174 | 169 | -6 | 252 | 36 | Bad |
748 | 841 | 871 | 706 | 780 | -463 | -230 | 20 | -53 | -90 | 322 | -8 | 248 | 18 | Good |
756 | 844 | 882 | 708 | 782 | -512 | -101 | -333 | -72 | -256 | 47 | -9 | 247 | 15 | Good |
756 | 844 | 883 | 710 | 787 | -512 | -143 | -290 | -114 | -256 | 87 | -7 | 247 | 18 | Good |
750 | 853 | 869 | 708 | 779 | -495 | -106 | -64 | -80 | -17 | 233 | -9 | 250 | 15 | Good |
752 | 846 | 880 | 708 | 792 | -511 | 66 | -8 | -184 | 167 | 71 | -8 | 249 | 15 | Good |
749 | 848 | 896 | 707 | 786 | -512 | -102 | -339 | -88 | -248 | 61 | -8 | 247 | 15 | Good |
750 | 840 | 883 | 714 | 794 | -511 | -27 | 60 | -211 | 159 | 175 | -10 | 248 | 17 | Good |
766 | 826 | 876 | 710 | 801 | -512 | 157 | -155 | -91 | 52 | -88 | -6 | 248 | 18 | Good |
762 | 843 | 872 | 704 | 784 | -512 | 181 | -81 | -234 | 118 | -4 | -8 | 249 | 16 | Good |
753 | 848 | 872 | 708 | 786 | -397 | 196 | -52 | -13 | 220 | 37 | -9 | 249 | 14 | Good |
752 | 839 | 886 | 705 | 787 | -512 | 27 | -193 | -219 | 2 | -13 | -9 | 250 | 18 | Good |
749 | 845 | 880 | 712 | 785 | -513 | -103 | -325 | -81 | -252 | 49 | -8 | 249 | 15 | Good |
753 | 843 | 879 | 707 | 785 | -387 | 174 | -131 | 7 | 178 | 70 | -7 | 246 | 14 | Good |
753 | 844 | 876 | 697 | 786 | -512 | 307 | -42 | -128 | 252 | -49 | -7 | 248 | 16 | Good |
756 | 842 | 874 | 708 | 796 | -488 | -139 | -82 | -57 | -63 | 234 | -8 | 248 | 17 | Good |
744 | 840 | 882 | 708 | 790 | -512 | -101 | -342 | -69 | -255 | 43 | -9 | 247 | 16 | Good |
754 | 849 | 871 | 704 | 781 | -512 | 246 | -159 | -89 | 96 | -136 | -7 | 248 | 17 | Good |
757 | 844 | 871 | 708 | 789 | -513 | 316 | -258 | -167 | 147 | -155 | -8 | 249 | 16 | Good |
747 | 839 | 851 | 705 | 789 | -455 | -115 | -66 | -50 | -49 | 249 | -8 | 247 | 14 | Good |
759 | 849 | 879 | 707 | 791 | -513 | 73 | -8 | -217 | 108 | 90 | -7 | 247 | 16 | Good |
753 | 849 | 881 | 717 | 793 | -216 | 178 | -62 | -196 | 175 | -38 | -6 | 260 | 18 | Hungry |
759 | 850 | 887 | 718 | 795 | 10 | -237 | 102 | -16 | -54 | 235 | -2 | 260 | 20 | Hungry |
771 | 855 | 887 | 718 | 795 | 41 | -180 | 166 | -10 | -15 | 244 | -4 | 261 | 17 | Hungry |
757 | 854 | 894 | 717 | 795 | -137 | -124 | 144 | -273 | 39 | 79 | -4 | 262 | 16 | Hungry |
788 | 860 | 897 | 718 | 796 | -194 | -85 | 261 | -234 | 100 | 91 | -4 | 263 | 17 | Hungry |
762 | 852 | 883 | 718 | 795 | 63 | -226 | 118 | 17 | -41 | 251 | -5 | 263 | 18 | Hungry |
760 | 852 | 887 | 716 | 795 | -159 | -57 | 174 | -225 | 56 | 113 | -2 | 261 | 19 | Hungry |
783 | 856 | 887 | 716 | 795 | -28 | -234 | 128 | -53 | -11 | 234 | -2 | 260 | 18 | Hungry |
767 | 853 | 885 | 717 | 796 | 18 | -227 | 139 | -22 | -35 | 237 | -3 | 262 | 20 | Hungry |
762 | 851 | 882 | 719 | 794 | 49 | -233 | 130 | 1 | -41 | 246 | -4 | 262 | 16 | Hungry |
759 | 851 | 874 | 718 | 795 | 98 | -133 | 189 | 26 | -3 | 250 | -4 | 263 | 17 | Hungry |
761 | 852 | 883 | 720 | 796 | 47 | -223 | 129 | 1 | -32 | 249 | -6 | 263 | 19 | Hungry |
762 | 855 | 893 | 719 | 793 | -121 | -65 | 121 | -215 | 77 | 101 | -4 | 261 | 18 | Hungry |
766 | 855 | 891 | 718 | 793 | -305 | -29 | 183 | -267 | 147 | 159 | -4 | 261 | 17 | Hungry |
764 | 853 | 891 | 719 | 793 | 32 | -128 | -37 | -31 | 9 | 209 | -5 | 262 | 17 | Hungry |
766 | 855 | 889 | 716 | 795 | -160 | -274 | 107 | -95 | -65 | 215 | -6 | 261 | 16 | Hungry |
774 | 856 | 893 | 721 | 796 | -181 | -43 | 130 | -246 | 80 | 98 | -4 | 262 | 19 | Hungry |
761 | 851 | 886 | 716 | 796 | 5 | -231 | 108 | -22 | -50 | 237 | -4 | 263 | 16 | Hungry |
760 | 851 | 890 | 716 | 793 | 2 | -236 | 109 | -13 | -47 | 235 | -3 | 263 | 19 | Hungry |
753 | 849 | 880 | 715 | 793 | -215 | 177 | -63 | -197 | 177 | -39 | -4 | 260 | 17 | Hungry |
769 | 846 | 881 | 768 | 791 | -67 | -228 | -130 | -34 | -246 | 75 | -9 | 250 | 40 | Me |
777 | 847 | 876 | 799 | 791 | -60 | -243 | -125 | -22 | -246 | 95 | -8 | 252 | 42 | Me |
777 | 846 | 876 | 798 | 790 | -59 | -241 | -125 | -21 | -248 | 94 | -8 | 252 | 43 | Me |
767 | 850 | 884 | 798 | 788 | -70 | -235 | -125 | -38 | -242 | 92 | -9 | 252 | 43 | Me |
758 | 849 | 884 | 797 | 789 | -48 | -240 | -143 | -4 | -245 | 109 | -8 | 252 | 43 | Me |
771 | 848 | 874 | 791 | 788 | -81 | -224 | -138 | -55 | -246 | 72 | -8 | 250 | 41 | Me |
766 | 852 | 884 | 814 | 796 | -168 | -256 | 141 | -177 | 6 | 203 | -9 | 250 | 41 | Me |
766 | 842 | 886 | 803 | 786 | -90 | -241 | -113 | -58 | -229 | 125 | -8 | 250 | 42 | Me |
785 | 849 | 893 | 778 | 779 | -237 | 167 | 80 | -246 | 164 | -118 | -9 | 251 | 42 | Me |
751 | 847 | 883 | 799 | 789 | -114 | -199 | -155 | -73 | -250 | 53 | -8 | 251 | 41 | Me |
766 | 854 | 880 | 788 | 791 | -96 | -211 | -143 | -65 | -236 | 86 | -9 | 252 | 42 | Me |
758 | 867 | 884 | 762 | 775 | -225 | -152 | -96 | -182 | -149 | 72 | -7 | 250 | 41 | Me |
763 | 811 | 877 | 790 | 783 | -182 | -314 | -105 | -142 | -280 | 157 | -7 | 250 | 39 | Me |
765 | 839 | 885 | 789 | 789 | -188 | -235 | -37 | -210 | -86 | 188 | -10 | 253 | 43 | Me |
758 | 846 | 877 | 800 | 790 | -100 | -219 | -134 | -63 | -244 | 78 | -8 | 250 | 41 | Me |
775 | 855 | 887 | 791 | 788 | -214 | 50 | 81 | -205 | 109 | -19 | -8 | 253 | 40 | Me |
768 | 847 | 884 | 796 | 788 | -91 | -229 | -127 | -55 | -241 | 83 | -7 | 250 | 41 | Me |
751 | 847 | 882 | 789 | 775 | -100 | -210 | -141 | -67 | -233 | 91 | -7 | 252 | 40 | Me |
762 | 849 | 883 | 775 | 782 | -99 | -216 | -141 | -67 | -237 | 90 | -10 | 252 | 43 | Me |
766 | 848 | 878 | 794 | 785 | -92 | -219 | -135 | -53 | -246 | 64 | -7 | 251 | 40 | Me |
741 | 845 | 868 | 708 | 796 | -474 | -130 | -82 | -45 | -81 | 231 | -8 | 249 | 14 | Null |
751 | 852 | 880 | 705 | 788 | -512 | -70 | -369 | -89 | -239 | 65 | -7 | 247 | 17 | Null |
779 | 841 | 877 | 855 | 790 | 102 | 78 | 191 | 71 | 250 | 63 | -6 | 251 | 37 | Null |
747 | 845 | 880 | 707 | 786 | -512 | -1 | -9 | -172 | 130 | 139 | -9 | 249 | 14 | Null |
748 | 840 | 874 | 803 | 785 | -197 | 141 | -78 | -191 | -11 | -176 | -6 | 250 | 37 | Null |
752 | 841 | 879 | 848 | 786 | -240 | -37 | 63 | -256 | -1 | 47 | -8 | 251 | 36 | Null |
750 | 857 | 886 | 708 | 786 | -512 | -71 | -375 | -91 | -246 | 56 | -8 | 248 | 16 | Null |
742 | 838 | 879 | 788 | 784 | -187 | -45 | 143 | -238 | 49 | 87 | -6 | 250 | 37 | Null |
759 | 843 | 870 | 705 | 785 | -496 | -132 | -95 | -59 | -63 | 228 | -7 | 248 | 17 | Null |
747 | 837 | 886 | 801 | 794 | -198 | 138 | -78 | -193 | -10 | -173 | -6 | 250 | 37 | Null |
747 | 839 | 871 | 796 | 784 | -159 | -99 | 148 | -213 | 43 | 129 | -6 | 250 | 38 | Null |
745 | 842 | 863 | 710 | 786 | -475 | -123 | -84 | -50 | -78 | 225 | -8 | 249 | 15 | Null |
750 | 854 | 875 | 720 | 787 | -63 | -203 | 133 | -100 | -10 | 222 | -7 | 249 | 13 | Null |
767 | 836 | 884 | 758 | 811 | -75 | -241 | -123 | -33 | -249 | 84 | -8 | 251 | 41 | Null |
773 | 831 | 883 | 775 | 783 | -120 | -225 | -144 | -99 | -255 | 90 | -8 | 251 | 37 | Null |
754 | 832 | 863 | 717 | 791 | -512 | -118 | -83 | -87 | -16 | 236 | -9 | 249 | 16 | Null |
743 | 852 | 882 | 706 | 783 | -126 | -215 | -118 | -94 | -229 | 90 | -6 | 250 | 36 | Null |
757 | 859 | 868 | 713 | 789 | 96 | -41 | -76 | -63 | -5 | 131 | -8 | 248 | 15 | Null |
755 | 851 | 868 | 711 | 789 | -512 | -110 | -105 | -119 | 5 | 221 | -9 | 248 | 16 | Null |
776 | 839 | 877 | 720 | 796 | -452 | -134 | -40 | -21 | 218 | 75 | -10 | 249 | 17 | Null |
End of preview. Expand
in Data Studio
Sensor-Based Motion Data Dataset
Description
This dataset contains sensor-based motion data collected from multiple files, each representing different recording sessions. It captures acceleration readings from various body parts, making it valuable for human activity recognition, biomechanics analysis, and motion classification.
Dataset Details
Columns:
- F1, F2, F3, F4, F5 – Feature values representing signal intensities or raw sensor readings.
- Acc_Fin_x, Acc_Fin_y, Acc_Fin_z – Accelerometer readings from the fingers in x, y, and z directions.
- Acc_Palm_x, Acc_Palm_y, Acc_Palm_z – Accelerometer readings from the palm in x, y, and z directions.
- Acc_Arm_x, Acc_Arm_y, Acc_Arm_z – Accelerometer readings from the arm in x, y, and z directions.
Notes:
- The dataset consists of multiple files, each containing sensor readings over time.
- Values are likely recorded at a fixed sampling rate, making the dataset useful for time-series analysis.
- The dataset can be applied to motion recognition, gesture classification, and biomechanical research.
Use Cases
- Human activity recognition – Classify different hand and arm movements.
- Gesture-based interface development – Use motion data for interactive systems.
- Sports and rehabilitation analytics – Analyze motion patterns for performance and recovery tracking.
- Machine learning applications – Train models for predictive motion analysis.
How to Use
You can load the dataset using the datasets library:
from datasets import load_dataset
dataset = load_dataset("Tarakeshwaran/Hackathon-Dataset_Round_2_test")
print(dataset)
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