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 |
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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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