Correction: Rucco, R.; et al. Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review. Sensors 2018, 18, 1613
by
, and
Rosaria Rucco
1,2,*
,
Antonietta Sorriso
3,
Marianna Liparoti
1,2,
Giampaolo Ferraioli
4,
Pierpaolo Sorrentino
2,3,
Michele Ambrosanio
3 and
Fabio Baselice
3 1
Department of Motor Sciences and Wellness, University of Naples “Parthenope”, 80133 Naples, Italy
2
IDC Hermitage Capodimonte, 80133 Naples, Italy
3
Department of Engineering, University of Naples “Parthenope”, 80133 Naples, Italy
4
Department of Science and Technologies, University of Naples “Parthenope”, 80133 Naples, Italy
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(8), 2462; https://doi.org/10.3390/s18082462
Submission received: 23 July 2018
/
Revised: 26 July 2018
/
Accepted: 27 July 2018
/
Published: 30 July 2018
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
The authors wish to make a correction to their paper [1]. The following Table 1 should be replaced with the table shown below it.
The authors would like to apologize for any inconvenience caused to the readers by these changes. The changes do not affect the scientific results. The manuscript will be updated and the original will remain online on the article webpage, with a reference to this Correction.
References
- Rucco, R.; Sorriso, A.; Liparoti, M.; Ferraioli, G.; Sorrentino, P.; Ambrosanio, M.; Baselice, F. Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review. Sensors 2018, 18, 1613. [Google Scholar] [CrossRef] [PubMed]
Table 1.
Summary of the wearable sensor-based systems for stability control in elderly people for the considered bibliographic research. Task types include the main activities proposed in the articles both for the dynamic as well as static analyses and reported in Tables 2 and 3. In some cases, both methodologies have been adopted. The manuscripts have been classified according to the main identified aims, i.e. fall risk assessment (FRA), fall detection (FD) and fall prevention (FP). Acronyms for the Validation column: ACC = accuracy, Sens = sensitivity, Spec = specificity, PFA = Probability of false alarm, Pc = Probability of correct decision. Acronyms for the Analysis column: Dyn = Dynamic.
Table 1.
Summary of the wearable sensor-based systems for stability control in elderly people for the considered bibliographic research. Task types include the main activities proposed in the articles both for the dynamic as well as static analyses and reported in Tables 2 and 3. In some cases, both methodologies have been adopted. The manuscripts have been classified according to the main identified aims, i.e. fall risk assessment (FRA), fall detection (FD) and fall prevention (FP). Acronyms for the Validation column: ACC = accuracy, Sens = sensitivity, Spec = specificity, PFA = Probability of false alarm, Pc = Probability of correct decision. Acronyms for the Analysis column: Dyn = Dynamic.
| Author (Year) | Participants (Number/Age) | Number of Sensors | Sensor Type | Sensor Position | Task Type | Goals | Validation | Analysis |
|---|---|---|---|---|---|---|---|---|
| Aloqlah (2010) [63] | (3/n.a.) | 1 | A | HD | STN | FP, FRA | ACC ≈ 95% | Both |
| Aminian (2011) [42] | (10/26.1 ± 2.8)&(10/71 ± 4.6) | 3 | A, P, G | FT | SW | FP | Sens = 93%, Spec = 100% | Dyn |
| Bertolotti (2016) [64] | (18/n.a.) | 4 | A, P, G, M | TR, AR | SU, SD, B | FD | n.a. | Dyn |
| Bounyong (2016) [43] | (52/72 ± 6.1) | 2 | A | LG | SW | FRA | ACC = 65% | Dyn |
| Caldara (2015) [65] | (5/31 ± 6)&(4/70.8 ± 7) | 4 | A, P, G, M | TR | SW | FD, FP, FRA | n.a. | Dyn |
| Chen (2010) [66] | (1/n.a.) | 1 | A | FT | SW | FP | Pc = 86% | Dyn |
| Cheng (2013) [67] | (10/24 ± 2) | 2 | A, EMG | LG | SW, SU, SD | FD | Sens = 95.33%, Spec = 97.66% | Dyn |
| Cola (2015) [68] | (30/32.9 ± 12.2) | 1 | A | TR | SW | FD, FRA | ACC = 84% | Dyn |
| Crispim-Junior (2013) [69] | (29/65) | 1 | C | EXT | SW, DA | FD | Sens = 88.33% | Dyn |
| Curone (2010) [70] | (6/29.5) | 1 | A | TR | SU, SD, SW | FD | Pc ≥ 90% | Both |
| De la Guia Solaz (2010) [71] | (10/23.7 ± 2.2)&(10/77.2 ± 4.3) | 2 | A, P | TR | SU, SD, SW, F | FD | ACC = 100%, Pc = 93%, PFA = 29% | Dyn |
| Deshmukh (2012) [40] | (4/n.a.) | 3 | A, G, M | LG | STN | FRA | n.a. | Static |
| Di Rosa (2017) [72] | (29/71.1 ± 6.9) | 2 | A, P | FT | DA | FRA | ACC = 95% | Dyn |
| Diraco (2014) [73] | (18/38 ± 6) | 1 | T | EXT | STN | FD | Pc > 83% | Static |
| Fernandez-Luque (2010) [74] | (n.a./n.a.) | 4 | A, P, M, IR | EXT | DA | FD, FRA | n.a. | Dyn |
| Ganea (2012) [75] | (35/54.2 ± 5.7) | 2 | A, G | TR, LG | SU, SD | FD, FP, FRA | ACC = 95% | Dyn |
| Gopalai (2011) [76] | (12/23.45 ± 1.45) | 2 | A, G | TR | STN | FP, FRA | n.a. | n.a. |
| Greene (2011) [77] | (114/71 ± 6.6) | 2 | A, G | LG | SW | FD | n.a. | Dyn |
| Hegde (2015) [78] | (n.a./n.a.) | 3 | A, P, G | FT | n.a. | FD, FRA | n.a. | Dyn |
| Howcroft (2017) [79] | (100/75.5 ± 6.7) | 2 | A, P | TR, HD, LG, FT | SW | FP, FRA | ACC = 78%, Sens = 26%, Spec = 95% | Dyn |
| Howcroft (2017) [80] | (76/75.2 ± 6.6) | 2 | A, P | TR, HD, LG, FT | SW, DW | FP, FRA | ACC = 57%, Sens = 43%, Spec = 65% | Dyn |
| Howcroft (2016) [81] | (100/75.5 ± 6.7) | 2 | A, P | TR, HD, LG, FT | SW, DW | FD, FP, FRA | n.a. | Dyn |
| Jian (2015) [82] | (8/33) | 2 | A, G | TR | F | FD | n.a. | Dyn |
| Jiang (2011) [83] | (48/40) | 3 | A, P, C | n.a. | SW, STN | FP, FRA | n.a. | Dyn |
| Karel (2010) [84] | (41/24 ± 4)&(50/67 ± 5) | 1 | A | TR | SW | FD | Sens = 98.4%, Spec = 99.9% | Dyn |
| Micó-Amigo (2016) [85] | (20/73.7 ± 7.9) | 2 | A, G | TR, LG | SW | FD, FP, FRA | Sens = 92.6 ÷ 98.2% | Dyn |
| Najafi (2002) [86] | (11/79 ± 6) | 1 | G | TR | SU, SD | FRA | Sens ≥ 95%, Spec ≥ 95% | Dyn |
| Ozcan (2016) [87] | (n.a./n.a.) | 2 | A, G | TR | n.a. | FD | Sens = 6.36%, Spec = 92.45% | Static |
| Paoli (2011) [88] | (1/n.a.) | >4 | A, P, M, IR | TR | DA | FD | n.a. | Both |
| Qu (2016) [89] | (10/25) | 1 | A | TR | F | FD | ROC curve | Dyn |
| Sazonov (2013) [90] | (1/n.a.) | 2 | A, P | FT | STN, STT, SW | FD, FRA | n.a. | Both |
| Simila (2017) [41] | (42/74.17 ± 5.57) | 1 | A | TR | SW | FP, FRA | Sens = 80%, Spec = 73% | Dyn |
| Stone (2013) [91] | (15/67) | 1 | K | n.a. | SW | FD | n.a. | Dyn |
| Szurley (2009) [92] | (n.a./n.a.) | 1 | A | TR | n.a. | FP | n.a. | Dyn |
| Tamura (2005) [93] | (6/66.3 ± 5) | 1 | A | TR | SU, SD | FD | Pc = 86% | Dyn |
| Tang (2016) [94] | (1/n.a.) | 1 | R | LG | SW, STR | FD, FP | n.a. | Dyn |
| Turcato (2010) [39] | (5/26 ± 6) | 2 | A, W | TR | STN | FP | ACC = 55–70% | Static |
| Van de Ven (2015) [95] | (1 /n.a.) | 2 | A, P | FT | STN, STT | FD | n.a. | Dyn |
| van Schooten (2016) [96] | (319/75.5 ± 6.9) | 1 | A | TR | DA | FD, FP, FRA | n.a. | Dyn |
| Vincenzo (2016) [97] | (57/74.35 ± 6.53) | 1 | A | TR | STN | FD | n.a. | Static |
| Yao (2015) [98] | (9/25) | 3 | A, G, M | TR | SW, F, R | FD, FP, FRA | n.a. | Dyn |
| Yuan (2015) [99] | (n.a./n.a.) | 2 | A, G | TR | F, STT, L | FD | n.a. | Both |
Table 1.
Summary of the wearable sensor-based systems for stability control in elderly people for the considered bibliographic research. Task types include the main activities proposed in the articles both for the dynamic as well as static analyses and reported in Tables 2 and 3. In some cases, both methodologies have been adopted. The manuscripts have been classified according to the main identified aims, i.e. fall risk assessment (FRA), fall detection (FD) and fall prevention (FP). Acronyms for the Validation column: ACC = accuracy, Sens = sensitivity, Spec = specificity, PFA = Probability of false alarm, Pc = Probability of correct decision. Acronyms for the Analysis column: Dyn = Dynamic.
Table 1.
Summary of the wearable sensor-based systems for stability control in elderly people for the considered bibliographic research. Task types include the main activities proposed in the articles both for the dynamic as well as static analyses and reported in Tables 2 and 3. In some cases, both methodologies have been adopted. The manuscripts have been classified according to the main identified aims, i.e. fall risk assessment (FRA), fall detection (FD) and fall prevention (FP). Acronyms for the Validation column: ACC = accuracy, Sens = sensitivity, Spec = specificity, PFA = Probability of false alarm, Pc = Probability of correct decision. Acronyms for the Analysis column: Dyn = Dynamic.
| Author (Year) | Participants (Number/Age) | Number of Sensors | Sensor Type | Sensor Position | Task Type | Goals | Validation | Analysis |
|---|---|---|---|---|---|---|---|---|
| Aloqlah (2010) [63] | (3/n.a.) | 1 | A | HD | STN | FP, FRA | ACC ≈ 95% | Both |
| Aminian (2011) [42] | (10/26.1 ± 2.8)&(10/71 ± 4.6) | 3 | A, P, G | FT | SW | FP | Sens = 93%, Spec = 100% | Dyn |
| Bertolotti (2016) [64] | (18/n.a.) | 4 | A, P, G, M | TR, AR | SU, SD, B | FD | n.a. | Dyn |
| Bounyong (2016) [43] | (52/72 ± 6.1) | 2 | A | LG | SW | FRA | ACC = 65% | Dyn |
| Caldara (2015) [65] | (5/31 ± 6)&(4/70.8 ± 7) | 4 | A, P, G, M | TR | SW | FD, FP, FRA | n.a. | Dyn |
| Chen (2010) [66] | (1/n.a.) | 1 | A | FT | SW | FP | Pc = 86% | Dyn |
| Cheng (2013) [67] | (10/24 ± 2) | 2 | A, EMG | LG | SW, SU, SD | FD | Sens = 95.33%, Spec = 97.66% | Dyn |
| Cola (2015) [68] | (30/32.9 ± 12.2) | 1 | A | TR | SW | FD, FRA | ACC = 84% | Dyn |
| Crispim-Junior (2013) [69] | (29/65) | 1 | C | EXT | SW, DA | FD | Sens = 88.33% | Dyn |
| Curone (2010) [70] | (6/29.5) | 1 | A | TR | SU, SD, SW | FD | Pc ≥ 90% | Both |
| De la Guia Solaz (2010) [71] | (10/23.7 ± 2.2)&(10/77.2 ± 4.3) | 2 | A, P | TR | SU, SD, SW, F | FD | ACC 100%, Pc = 93%, PFA = 29% | Dyn |
| Deshmukh (2012) [40] | (4/n.a.) | 3 | A, G, M | LG | STN | FRA | n.a. | Static |
| Di Rosa (2017) [72] | (29/71.1 ± 6.9) | 2 | A, P | FT | DA | FRA | ACC = 95% | Dyn |
| Diraco (2014) [73] | (18/38 ± 6) | 1 | T | EXT | STN | FD | Pc > 83% | Static |
| Fernandez-Luque (2010) [74] | (n.a./n.a.) | 4 | A, P, M, IR | EXT | DA | FD, FRA | n.a. | Dyn |
| Ganea (2012) [75] | (35/54.2 ± 5.7) | 2 | A, G | TR, LG | SU, SD | FD, FP, FRA | ACC = 95% | Dyn |
| Gopalai (2011) [76] | (12/23.45 ± 1.45) | 2 | A, G | TR | STN | FP, FRA | n.a. | n.a. |
| Greene (2011) [77] | (114/71 ± 6.6) | 2 | A, G | LG | SW | FD | n.a. | Dyn |
| Hegde (2015) [78] | (n.a./n.a.) | 3 | A, P, G | FT | n.a. | FD, FRA | n.a. | Dyn |
| Howcroft (2017) [79] | (100/75.5 ± 6.7) | 2 | A, P | TR, HD, LG, FT | SW | FP, FRA | ACC = 78%, Sens = 26%, Spec = 95% | Dyn |
| Howcroft (2017) [80] | (76/75.2 ± 6.6) | 2 | A, P | TR, HD, LG, FT | SW, DW | FP, FRA | ACC = 57%, Sens = 43%, Spec = 65% | Dyn |
| Howcroft (2016) [81] | (100/75.5 ± 6.7) | 2 | A, P | TR, HD, LG, FT | SW, DW | FD, FP, FRA | n.a. | Dyn |
| Jian (2015) [82] | (8/33) | 2 | A, G | TR | F | FD | n.a. | Dyn |
| Jiang (2011) [83] | (48/40) | 3 | A, P, C | n.a. | SW, STN | FP, FRA | n.a. | Dyn |
| Karel (2010) [84] | (41/24 ± 4)&(50/67 ± 5) | 1 | A | TR | SW | FD | Sens = 98.4%, Spec =99.9% | Dyn |
| Micó-Amigo (2016) [85] | (20/73.7 ± 7.9) | 2 | A, G | TR, LG | SW | FD, FP, FRA | n.a. | Dyn |
| Najafi (2002) [86] | (11/79 ± 6) | 1 | G | TR | SU, SD | FRA | Sens ≥ 95%, Spec ≥ 95% | Dyn |
| Ozcan (2016) [87] | (n.a./n.a.) | 2 | A, G | TR | n.a. | FD | Sens = 96.36%, Spec = 92.45% | Static |
| Paoli (2011) [88] | (1/n.a.) | >4 | A, P, M, IR | TR | DA | FD | n.a. | Both |
| Qu (2016) [89] | (10/25) | 1 | A | TR | F | FD | ROC curve | Dyn |
| Sazonov (2013) [90] | (1/n.a.) | 2 | A, P | FT | STN, STT, SW | FD, FRA | n.a. | Both |
| Simila (2017) [41] | (42/74.17 ± 5.57) | 1 | A | TR | SW | FP, FRA | Sens = 80%, Spec = 73% | Dyn |
| Stone (2013) [91] | (15/67) | 1 | K | n.a. | SW | FD | n.a. | Dyn |
| Szurley (2009) [92] | (n.a./n.a.) | 1 | A | TR | n.a. | FP | n.a. | Dyn |
| Tamura (2005) [93] | (6/66.3 ± 5) | 1 | A | TR | SU, SD | FD | Pc = 86% | Dyn |
| Tang (2016) [94] | (1/n.a.) | 1 | R | LG | SW, STR | FD, FP | n.a. | Dyn |
| Turcato (2010) [39] | (5/26 ± 6) | 2 | A, W | TR | STN | FP | ACC = 55–70% | Static |
| Van de Ven (2015) [95] | (1 /n.a.) | 2 | A, P | FT | STN, STT | FD | n.a. | Dyn |
| van Schooten (2016) [96] | (319/75.5 ± 6.9) | 1 | A | TR | DA | FD, FP, FRA | n.a. | Dyn |
| Vincenzo (2016) [97] | (57/74.35 ± 6.53) | 1 | A | TR | STN | FD | n.a. | Static |
| Yao (2015) [98] | (9/25) | 3 | A, G, M | TR | SW, F, R | FD, FP, FRA | n.a. | Dyn |
| Yuan (2015) [99] | (n.a./n.a.) | 2 | A, G | TR | F, STT, L | FD | n.a. | Both |
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Rucco, R.; Sorriso, A.; Liparoti, M.; Ferraioli, G.; Sorrentino, P.; Ambrosanio, M.; Baselice, F. Correction: Rucco, R.; et al. Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review. Sensors 2018, 18, 1613. Sensors 2018, 18, 2462. https://doi.org/10.3390/s18082462
AMA Style
Rucco R, Sorriso A, Liparoti M, Ferraioli G, Sorrentino P, Ambrosanio M, Baselice F. Correction: Rucco, R.; et al. Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review. Sensors 2018, 18, 1613. Sensors. 2018; 18(8):2462. https://doi.org/10.3390/s18082462
Chicago/Turabian StyleRucco, Rosaria, Antonietta Sorriso, Marianna Liparoti, Giampaolo Ferraioli, Pierpaolo Sorrentino, Michele Ambrosanio, and Fabio Baselice. 2018. "Correction: Rucco, R.; et al. Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review. Sensors 2018, 18, 1613" Sensors 18, no. 8: 2462. https://doi.org/10.3390/s18082462
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