Table A.1.
Sensor-based HAR
| C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | |
|---|---|---|---|---|---|---|---|---|
| DS& | Activities | Datasets | Subjects | Scenarios | Total # actions | Performance evaluation | CIT* | |
| R1 | WS | ADL: 10, Sports: 11 | Proprietary dataset | 13- 10 M and 3 F | RT, Sports | 1300/1100 | 99.65%, 99.92% | Hsu et al. (2018) |
| R2 | SPS | ADL: 6, 5 | UCI-HAR, Weakly labelled (WL) | 30 subs (UCI), 7 participants (WL) | Waist mounted SPS | 76,157 | UCIHAR- 93.41%, WL- 93.83% | Wang et al. (2019a) |
| R3 | SPS | ADL: 7 | Proprietary dataset | 100 participants | Texting, handheld, trouser pocket, backpack | 235 977 Samples | CNN-7–95.06%, Ensemble-96.11% | Zhu et al. (2019) |
| R4 | SPS | ADL: 4 | Proprietary dataset | Active: 147, inactive: 99, walking: 200 and driving: 120. Total 574 | Lab env | 4,99,276 | Mean accuracy-74.39% | Garcia-Gonzalez et al. (2020) |
| R5 | SPS | ADL: 6 | HHAR | 9 subs | Biking, walking, stairs | 4,39,30,257 | 96.79% | Sundaramoorthy and Gudur (2018) |
| R6 | SPS | ADL: 6, 8, 9 | HHAR, RWHAR, MobiAct | 9, 15 (M-8, F-7), 57 (M-42, F-15) | Indoor and outdoor | 4,39,30,257/2500 | F1-measure on three datasets | Gouineua et al. (2018) |
| R7 | SPS | ADL: 8 | RWHAR | 15 subs | Experimental setup | 8,85,360 | F1-Score:0.94 | Lawal and Bano 2020) |
| R8 | SPS | ADL: 6, 9, 14 | HHAR, PAMAP, USC-HAD | 9 (HHAR), 15 (RWHAR) | Waist mounted SPS, Experimental setup | 4,39,30,257/38,50,505 | F1-score: 0.848, 0.723, 0.702 | Buffelli and Vandin (2020) |
| R9 | SPS | ADL: 6 | HHAR | 9 subs | Biking, walking, stairs | 4,39,30,257 | 98% | Yao et al. (2019) |
| R10 | SPS | ADL: 6 | HHAR, carTrack | 9 subs | Biking, walking, stairs | 4,39,30,257 | 94.2% | Yao et al. (2017) |
| R11 | WS, SPS | ADL: 6, 7, 17 | UCIHAR, WISDM, OPPORTUNITY | 30 (UCI-HAR), 4 (OPPORTUNITY), 51 (WISDM) | Waist mounted SPS, Experimental setup, Biking, walking, stairs | 76,157/2,551/15,630,426 | F1-score: 92.63%, 95.85%, 95.78% | Xia et al. (2020) |
| R12 | SPS | ADL: 13, 5, 6 | UniMibShar, MobiAct | 57 (MobiAct) | Fall scenario | 11,771/2500 | 87.30 | Ferrari et al. (2020) |
| R13 | SPS | ADL: 50 | Vaizman dataset | 60 subs | Indoor, running (Phone in pocket) | 300 k | 92.80 | Fazli et al. (2021) |
| R14 | WS | 19 (ADL) | 19 NonSense | 13 subs (5 Female 8 male) of age (19–45 years) | 9 activities indoor and 9 outdoor | _ | Precision: 93.41%, recall: 93.16% | Pham et al. (2020) |
| R15 | WS | ADL: (6, 12,6,11), GC | UCI-HAR, DaphNet, OPPORTUNITY, Skoda | 30 (UCI-HAR), 4 (OPPORTUNITY) | ADL (UCI-HAR & Opportunity) Skoda: GC | 76,157/15,630,426 | 96.7%, 97.8%, 92.5%, 94.1%, 92.6% | Murad and Pyun (2017) |
| R16 | WS | 18 ADL | 19 NonSense | 13 subs of age (19–45 years) | 9 indoor and 9 outdoor | – | F1-score: 77.7 | Pham et al. (2017) |
*CIT citation
&DS data source
WS wearable sensor, SPS smartphone sensor, Acc accelerometer, Gyro gyroscope, Mag magnetometer, ADL activities of daily living, RT Rrutine tasks, GC gestures in car maintenance