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
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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
The authors wish to make a correction to their paper []. The following Table 1 should be replaced with the table shown below it.
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.
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. Sensors2018, 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.
