Table 4.

Sensor-assisted treatment.

Author	Participants	UE disability	Sensor information	Data collection/treatment	Data processing	Data analysis	Results
Chae et al. [70]	N=23 stroke, chronic n=6 control group (no feedback) n=17 treatment group (feedback)	FMA = 29 (control group), 36.6 (treatment group)	Brand: LG smartwatch, IMU Number: 1 Placement: affected wrist	Home: sensor data collected whenever smartphone app used by participants. Treatment: completed 4 home exercises daily over 12-weeks, treatment group received weekly feedback on type and amount of exercises they completed	Labeling: Home exercise tasks, observation notes Preprocessing: not specified	Model type: ML, convolutional neural network (deep learning), cross-validation testing Training: supervised Classification: home exercise tasks (shoulder flexion, wall pushups, scapular exercise, towel slides) Validation: out-of-subject testing	Accuracy: 0.99 (personal data) 0.95 (total data) Training time for home exercises (minutes per day): 13.6 control group (self-report) 22.6 treatment group(sensor data) Change in WMFT scores, baseline to discharge: 3.4 control 2.8 treatment Change in shoulder flexion, baseline to discharge (degrees): 5.5 control 20.2 treatment Change in shoulder internal rotation, baseline to discharge (degrees): 6.5 control 13.1 treatment
Da-Silva et al. [68]	N=11 stroke, acute, and subacute	ARAT = 39 (mean)	Brand: CueS, ACC Number: 1 Placement: affected wrist	Inpatient: 1 week to set prompt levels Home: 12 h/day ×4 weeks. Treatment: provided external vibration cue if UE activity did not reach customized threshold level	Labeling: NA Preprocessing: not specified	Metrics: Median signal vector magnitude (calculated every 3 days to set threshold for vibration cues)	Adherence to wearing sensors = 89% 11–29% increase in mean activity one-hour post vibration cue Cueing frequency preference higher for severe group and lower for mild group
Da-Silva et al. [69]	N=33 stroke, subacute n=19 control group(no vibration) n=14 treatment group (vibration)	ARAT = 15 (control group), 37 (treatment group)	Brand: CueS, ACC Number: 1 Placement: affected wrist	Inpatient and home: Sensors worn 12 h/day ×4 weeks. Treatment: provided external vibration cue if UE activity did not reach customized ACC threshold level	Labeling: NA Preprocessing: not specified	Metrics: Median signal vector magnitude (calculated every 3 days to set threshold for vibration cues)	Adherence to wearing sensors = 79% Responses to receiving wrist vibration cue (self-report): 43% practiced activities from daily activities list 38% practiced their own self-chosen activity 17% ignored the cue Cueing frequency preference: 8 cues per day, hourly basis
Lee et al. [72]	n=10 control n=20stroke, chronic	FMA = 37 (mean)	Brand: Shimmer, IMU Number: 2 Placement: bilateral wrist	Outpatient: ADLs targeting unilateral, bilateral and stabilization skills, strength and range of motion exercises Stakeholder survey of therapists (n=13) and stroke survivors (n=17) on user feasibility of proposed approach	Labeling: video Preprocessing: band-pass filtering	Model type: ML, logistic regression, nearest neighbor with dynamic time warping algorithm, random forest algorithm Training: supervised Classification: functional/non-functional movement, feedback/no feedback, types of feedback categories (compensatory or accuracy) Validation: out-of-subject testing	Accuracy, functional/non-functional movement classification: 0.87 control and stroke unaffected UE Sensitivity, feedback/no-feedback classification: 0.83 stroke Sensitivity, type of feedback, compensatory classification: 0.64 stroke Sensitivity, type of feedback, accuracy classification: 0.76 stroke Survey, therapists: 91.2% willing to use in clinical practice Survey, stroke survivors: 76.5% willing to use the system 88.2% willing to use the system with therapist recommendation Survey, stroke survivors, preferred method of receiving UE feedback: 29.4% visual 23.5% vibration 35.3% sound and vibration
Whitford et al. [71]	N=8 stroke, chronic	FMA = 32 (mean)	Brand: Actigraph, ACC Number: 2 Placement: bilateral wrist	Home: sensors worn for 3-week treatment period Treatment: 7 feedback sessions provided over 3 weeks on duration of use, acceleration magnitude, use ratio	Labeling: NA Preprocessing: built-in software	Metrics: Activity counts (proprietary software) Duration of use Use ratio Acceleration magnitude	Adherence to wearing sensors: 100% Perceived UE use at home: 1.93 MAL AOU baseline 2.84 MAL AOU discharge

ACC: accelerometer; ARAT: Action Research Arm Test; FMA: Fugl-Meyer Assessment; IMU: inertial measurement unit; MAL AOU: Motor Activity Log, Amount of Use Subscale; ML: machine learning; WMFT: Wolf Motor Function Test.

Participants: number of healthy and/or stroke participants, level of chronicity (acute: onset – 1 month, subacute 1–6 months, chronic >6 months); UE disability: motor impairment (FMA) or activity limitation (ARAT); sensor information: name and type of device, number and location of sensors placed on the UE; data collection/treatment: location and type of UE motion collected; summary of study intervention; data processing: methods for data labeling and preprocessing; data analysis: motion data metrics calculated OR model type (machine learning or non-machine learning), training type (supervised/unsupervised), classification of UE motion (performance feedback categories), and validation of algorithm (out-of-subject, in-subject testing); results: UE training augmentation, performance of algorithm (sensitivity or accuracy).