A Review of Wrist-Worn Wearable: Sensors, Models, and Challenges

Table 13

Experimental setup for WWD studies.

Study	Goal	Dataset (M : F)	Domain
[64]	Falling	12 (3 groups of 4 people of different ages).	Elderly people living
[65]	Step	1 Commercial devices: walk on the treadmill at 4.5 km/h; Prototype experiments: walk 120 seconds on the treadmill	Activity tracker (sport)
[69]	Step	1 Straight line walks of 30 steps at different paces	Activity tracker (sport)
[71]	22 Complex fine-grained activity contexts; (i) Locomotive (walk indoors and run indoors); (ii) semantic (use refrigerator, clean utensil, cooking, sit and eat, use bathroom sink, standing, and talking); (iii) transitional (indoor to outdoor, outdoor to indoor, walk upstairs, and walk downstairs); and (iv) postural/relatively stationary (just stand, stand and lean on wall, lying on bed, sit on bed, sit on desk chair, lying on floor, sit on floor, lying on sofa, sit on sofa, and sit on commode).	2 two separate home environments.; User 1: 22 activities, user 2: 19 activities.; Every user’s series of selected activities consisted of an average of 45 minutes of sensor data collection.	Home living
[16]	Keystrokes and web browsing	1 activity 1: 60 key presses per character, repeat in 3 days.; Activity 2: capturing the lux readings for 10 popular websites, each one minute	Security
[68]	Holding mobile phone on left or right	24 14: control study, 10 minutes each; 10: user study to receive feedback	Adaptive user interface: one-handed interaction
[2]	Cardiopulmonary resuscitation (CPR)	41 (24: 17) age (24-70, average: 37)	Health: training
[70]	Hair touch and restless leg movement	1 two different floorings: carpet and vinyl	Health: consumer care product research
[3]	Two eating activities	10 (7: 3) age (15-52, average: 28.7), four food groups: different chewing motions due to different food textures	Health: weight management
[66]	Walking, standing, sitting, and lying	3 triaxial acceleration data in the dataset of Activity Recognition Challenge [16], 5.5 seconds. 300,000 records or 2300 windows (window length is 128) for each wrist of a subject.	Home living
[6]	Writing a research paper	7 (2 users, 5 crowd workers), full weak	Education
[72]	Mood recognition	14 patients	Health: mood changes of bipolar disorder
[67]	Sitting, standing, household activities and stationary cycling with two intensities	25 healthy people	Home living
[4]	3 (neutral, happy, and angry)	123 (45: 78)	Daily living
[80]	7 (amusement, sadness, anger, fear, disgust, surprise, and neutral)	14 (5: 9) age (20-28)	Recommendation and sharing
[77]	Computed dental radiography (CDR)	40 (15: 25) average age (25: 29)	Health
[83]	8 (upset, stressed, tense, excited, happy, bored, tired, and relaxed)	18 (2: 16) (10 university students, 6 researchers/staff, 1 software engineer, 1 professor)	Daily living
[73]	16 (pride, elation, joy, satisfaction, relief, hope, interest, surprise, sadness, fear, shame, guilt, envy, disgust, contempt, and anger)	DEAP dataset	Social living
[76]	4 (calm, happy, fear, sad) Valence, Arousal, and Dominance (VAD)	12	Daily living
[79]	8 (excitement, happiness, calmness, tiredness, boredom, sadness, stress, and anger)	4	Office living
[75]	VAD (2: A: +/−, 3: V: +/−/0, 5: VA: 0−/++/−+/+−/−−, 10: VAD)	20 (9: 11); Age (22–76) (average age 47.4, ).; Right-handed, healthy, and had normal vision or corrected normal vision.	Work living
[78]	VAD; A: 0 (very calm) to 4 (very aroused); V: −2 (unpleasant) to 2 (very pleasant).; 7 (amusement, anger, disgust, excitement, fear, fun, and shock)	30: train DECAF dataset (VAD); 30: test 600 individual records.	Daily living
[81]	2 (frustration and satisfaction)	3 (friends and family), age (26, 24, and 44); Big data: 176 minutes of sampled data, 10,560 seconds of raw data, and electroencephalogram with 5 million lines	Daily living
[63]	N/A	N/A	Daily living
[74]	2 (mania and depression)	N/A	Mental health
[82]	Only propose	Only propose	Child care
[85]	Happy-sad	68	Daily living
[84]	VAD	15 (7 : 8)	Daily living
[5]	Low stress and high stress	15	Daily living